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                            [course_code] => bigdatar
                            [hr_nid] => 68924
                            [title] => Programming with Big Data in R
                            [requirements] => 
                            [overview] => <p>Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.</p>

                            [category_overview] => 
                            [outline] => <h3>Introduction to Programming Big Data with R (bpdR)</h3>

<ul>
	<li>Setting up your environment to use pbdR</li>
	<li>Scope and tools available in pbdR</li>
	<li>Packages commonly used with Big Data alongside pbdR</li>
</ul>

<h3>Message Passing Interface (MPI)</h3>

<ul>
	<li>Using pbdR MPI 5</li>
	<li>Parallel processing</li>
	<li>Point-to-point communication</li>
	<li>Send Matrices</li>
	<li>Summing Matrices</li>
	<li>Collective communication</li>
	<li>Summing Matrices with Reduce</li>
	<li>Scatter / Gather</li>
	<li>Other MPI communications</li>
</ul>

<h3>Distributed Matrices</h3>

<ul>
	<li>Creating a distributed diagonal matrix</li>
	<li>SVD of a distributed matrix</li>
	<li>Building a distributed matrix in parallel</li>
</ul>

<h3>Statistics Applications</h3>

<ul>
	<li>Monte Carlo Integration</li>
	<li>Reading Datasets</li>
	<li>Reading on all processes</li>
	<li>Broadcasting from one process</li>
	<li>Reading partitioned data</li>
	<li>Distributed Regression</li>
	<li>Distributed Bootstrap</li>
</ul>

                            [language] => en
                            [duration] => 21
                            [status] => published
                            [changed] => 1700037139
                            [source_title] => Programming with Big Data in R
                            [source_language] => en
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                    [1] => Array
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                                    [tid] => 766
                                    [alias] => big-data-training
                                    [name] => Big Data
                                    [english_name] => Big Data
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                            [1] => stdClass Object
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                                    [tid] => 877
                                    [alias] => r-language-training
                                    [name] => R Language
                                    [english_name] => R Language
                                    [consulting_option] => available
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                    [2] => bigdatar
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                                    [tidyverse] => stdClass Object
                                        (
                                            [course_code] => tidyverse
                                            [hr_nid] => 212656
                                            [title] => Introduction to Data Visualization with Tidyverse and R
                                            [requirements] => <ul>
	<li>No programming experience is necessary</li>
</ul>

                                            [overview] => <p>The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.</p>

<p>In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Perform data analysis and create appealing visualizations</li>
	<li>Draw useful conclusions from various datasets of sample data</li>
	<li>Filter, sort and summarize data to answer exploratory questions</li>
	<li>Turn processed data into informative line plots, bar plots, histograms</li>
	<li>Import and filter data from diverse data sources, including Excel, CSV, and SPSS files</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Beginners to the R language</li>
	<li>Beginners to data analysis and data visualization</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>Introduction</p>

<ul>
	<li>Tydyverse vs traditional R plotting</li>
</ul>

<p>Setting up your working environment</p>

<p>Preparing the dataset</p>

<p>Importing and filtering data</p>

<p>Wrangling the data</p>

<p>Visualizing the data (graphs, scatter plots)</p>

<p>Grouping and summarizing the data</p>

<p>Visualizing the data (line plots, bar plots, histograms, boxplots)</p>

<p>Working with non-standard data</p>

<p>Closing remarks</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037359
                                            [source_title] => Introduction to Data Visualization with Tidyverse and R
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
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                                            [use_mt] => stdClass Object
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                                            [cc] => tidyverse
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                                    [datavault] => stdClass Object
                                        (
                                            [course_code] => datavault
                                            [hr_nid] => 210132
                                            [title] => Data Vault: Building a Scalable Data Warehouse
                                            [requirements] => <ul>
	<li>An understanding of data warehousing concepts</li>
	<li>An understanding of database and data modeling concepts</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Data modelers</li>
	<li>Data warehousing specialist</li>
	<li>Business Intelligence specialists</li>
	<li>Data engineers</li>
	<li>Database administrators</li>
</ul>

                                            [overview] => <p>Data Vault Modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or &quot;all the data, all the time&quot;. Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema.</p>

<p>In this instructor-led, live training, participants will learn how to build a Data Vault.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.</li>
	<li>Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.</li>
	<li>Develop a consistent and repeatable ETL (Extract, Transform, Load) process.</li>
	<li>Build and deploy highly scalable and repeatable warehouses.</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn how to build a Data Vault.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.</li>
	<li>Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.</li>
	<li>Develop a consistent and repeatable ETL (Extract, Transform, Load) process.</li>
	<li>Build and deploy highly scalable and repeatable warehouses.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<ul>
	<li>The shortcomings of existing data warehouse data modeling architectures</li>
	<li>Benefits of Data Vault modeling</li>
</ul>

<p>Overview of Data Vault architecture and design principles</p>

<ul>
	<li>SEI / CMM / Compliance</li>
</ul>

<p>Data Vault applications</p>

<ul>
	<li>Dynamic Data Warehousing</li>
	<li>Exploration Warehousing</li>
	<li>In-Database Data Mining</li>
	<li>Rapid Linking of External Information</li>
</ul>

<p>Data Vault components</p>

<ul>
	<li>Hubs, Links, Satellites</li>
</ul>

<p>Building a Data Vault</p>

<p>Modeling Hubs, Links and Satellites</p>

<p>Data Vault reference rules</p>

<p>How components interact with each other</p>

<p>Modeling and populating a Data Vault</p>

<p>Converting 3NF OLTP to a Data Vault Enterprise Data Warehouse (EDW)</p>

<p>Understanding load dates, end-dates, and join operations</p>

<p>Business keys, relationships, link tables and join techniques</p>

<p>Query techniques</p>

<p>Load processing and query processing</p>

<p>Overview of Matrix Methodology</p>

<p>Getting data into data entities</p>

<p>Loading Hub Entities</p>

<p>Loading Link Entities</p>

<p>Loading Satellites</p>

<p>Using SEI/CMM Level 5 templates to obtain repeatable, reliable, and quantifiable results</p>

<p>Developing a consistent and repeatable ETL (Extract, Transform, Load) process</p>

<p>Building and deploying highly scalable and repeatable warehouses</p>

<p>Closing remarks</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1715349914
                                            [source_title] => Data Vault: Building a Scalable Data Warehouse
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
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                                            [use_mt] => stdClass Object
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                                                    [field_overview] => 
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                                                    [field_overview_in_category] => 
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                                            [cc] => datavault
                                        )

                                    [sparkstreaming] => stdClass Object
                                        (
                                            [course_code] => sparkstreaming
                                            [hr_nid] => 356863
                                            [title] => Spark Streaming with Python and Kafka
                                            [requirements] => <ul>
	<li>Experience with Python and Apache Kafka</li>
	<li>Familiarity with stream-processing platforms</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Data engineers</li>
	<li>Data scientists</li>
	<li>Programmers</li>
</ul>

                                            [overview] => <p>Apache Spark Streaming is a scalable, open source stream&nbsp;processing system that allows users to process real-time data from supported sources. Spark Streaming enables fault-tolerant processing of data streams.</p>

<p>This instructor-led, live training (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.</p>

<p>By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.</p>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.</p>

<p>By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.</p>

                                            [outline] => <p>Introduction</p>

<p>Overview of Spark Streaming Features and Architecture</p>

<ul>
	<li>Supported data sources</li>
	<li>Core APIs</li>
</ul>

<p>Preparing the Environment</p>

<ul>
	<li>Dependencies</li>
	<li>Spark and streaming context</li>
	<li>Connecting to Kafka</li>
</ul>

<p>Processing Messages</p>

<ul>
	<li>Parsing inbound messages as JSON</li>
	<li>ETL processes</li>
	<li>Starting the streaming context</li>
</ul>

<p>Performing a Windowed Stream Processing</p>

<ul>
	<li>Slide interval</li>
	<li>Checkpoint delivery configuration</li>
	<li>Launching the environment</li>
</ul>

<p>Prototyping the Processing Code</p>

<ul>
	<li>Connecting to a Kafka topic</li>
	<li>Retrieving JSON from data source using Paw</li>
	<li>Variations and additional processing</li>
</ul>

<p>Streaming the Code</p>

<ul>
	<li>Job control variables</li>
	<li>Defining values to match</li>
	<li>Functions and conditions</li>
</ul>

<p>Acquiring Stream Output</p>

<ul>
	<li>Counters</li>
	<li>Kafka output (matched and non-matched)</li>
</ul>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037710
                                            [source_title] => Spark Streaming with Python and Kafka
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
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                                            [cc] => sparkstreaming
                                        )

                                    [ksql] => stdClass Object
                                        (
                                            [course_code] => ksql
                                            [hr_nid] => 318463
                                            [title] => Confluent KSQL
                                            [requirements] => <ul>
	<li>Experience with Apache Kafka.</li>
	<li>Java programming experience.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations.</p>

<p>This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Confluent KSQL.</li>
	<li>Set up a stream processing pipeline using only SQL commands (no Java or Python coding).</li>
	<li>Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.</li>
	<li>Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Confluent KSQL.</li>
	<li>Set up a stream processing pipeline using only SQL commands (no Java or Python coding).</li>
	<li>Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.</li>
	<li>Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Setting up Confluent KSQL</p>

<p>Overview of KSQL Features and Architecture</p>

<p>How KSQL Interacts with Apache Kafka</p>

<p>Use Cases for KSQL</p>

<p>KSQL Command Line and Operations</p>

<p>Ingesting Data (CSV, JSON, etc.)</p>

<p>Creating a Stream</p>

<p>Creating a Table</p>

<p>Advanced KSQL Operations (Joins, Windowing, Aggregations, Geospatial, etc.)</p>

<p>Deploying KSQL to Production</p>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037528
                                            [source_title] => Confluent KSQL
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
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                                                    [field_overview] => 
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                                                    [field_overview_in_category] => 
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                                            [cc] => ksql
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                                    [apacheignite] => stdClass Object
                                        (
                                            [course_code] => apacheignite
                                            [hr_nid] => 209621
                                            [title] => Apache Ignite for Developers
                                            [requirements] => <ul>
	<li>An understanding of databases</li>
	<li>An understanding of Java</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale, and availability.</p>

<p>This instructor-led, live training (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.</li>
	<li>Achieve persistence without syncing data back to a relational database.</li>
	<li>Use Ignite to carry out SQL and distributed joins.</li>
	<li>Improve performance by moving data closer to the CPU, using RAM as a storage.</li>
	<li>Spread data sets across a cluster to achieve horizontal scalability.</li>
	<li>Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.</li>
	<li>Achieve persistence without syncing data back to a relational database.</li>
	<li>Use Ignite to carry out SQL and distributed joins.</li>
	<li>Improve performance by moving data closer to the CPU, using RAM as a storage.</li>
	<li>Spread data sets across a cluster to achieve horizontal scalability.</li>
	<li>Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Overview of Big Data Tools and Technologies</p>

<p>Installing and Configuring Apache Ignite</p>

<p>Overview of Ignite Architecture</p>

<p>Querying Data in Ignite</p>

<p>Spreading Large Data Sets across a Cluster</p>

<p>Understanding the In-Memory Data Grid</p>

<p>Writing a Service in Ignite</p>

<p>Running Distributed Computing with Ignite</p>

<p>Integrating Ignite with RDBMS, NoSQL, Hadoop and Machine Learning Processors</p>

<p>Testing and Troubleshooting</p>

<p>Summary and Next Steps</p>

                                            [language] => en
                                            [duration] => 14
                                            [status] => published
                                            [changed] => 1700037322
                                            [source_title] => Apache Ignite for Developers
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => apacheignite
                                        )

                                    [beam] => stdClass Object
                                        (
                                            [course_code] => beam
                                            [hr_nid] => 283646
                                            [title] => Unified Batch and Stream Processing with Apache Beam
                                            [requirements] => <ul>
	<li>Experience with Python Programming.</li>
	<li>Experience with the Linux command line.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It&#39;s power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam&#39;s supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system.</p>

<p>In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apache Beam.</li>
	<li>Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.</li>
	<li>Execute pipelines across multiple environments.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

<p><strong>Note</strong></p>

<ul>
	<li>This course will be available Scala in the future. Please contact us to arrange.</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>Introduction</p>

<ul>
	<li>Apache Beam vs MapReduce, Spark Streaming, Kafka Streaming, Storm and Flink</li>
</ul>

<p>Installing and Configuring Apache Beam</p>

<p>Overview of Apache Beam Features and Architecture</p>

<ul>
	<li>Beam Model, SDKs, Beam Pipeline Runners</li>
	<li>Distributed processing back-ends</li>
</ul>

<p>Understanding the Apache Beam Programming Model</p>

<ul>
	<li>How a pipeline is executed</li>
</ul>

<p>Running a sample pipeline</p>

<ul>
	<li>Preparing a WordCount pipeline</li>
	<li>Executing the Pipeline locally</li>
</ul>

<p>Designing a Pipeline</p>

<ul>
	<li>Planning the structure, choosing the transforms, and determining the input and output methods</li>
</ul>

<p>Creating the Pipeline</p>

<ul>
	<li>Writing the driver program and defining the pipeline</li>
	<li>Using Apache Beam classes</li>
	<li>Data sets, transforms, I/O, data encoding, etc.</li>
</ul>

<p>Executing the Pipeline</p>

<ul>
	<li>Executing the pipeline locally, on remote machines, and on a public cloud</li>
	<li>Choosing a runner</li>
	<li>Runner-specific configurations</li>
</ul>

<p>Testing and Debugging Apache Beam</p>

<ul>
	<li>Using type hints to emulate static typing</li>
	<li>Managing Python Pipeline Dependencies</li>
</ul>

<p>Processing Bounded and Unbounded Datasets</p>

<ul>
	<li>Windowing and Triggers</li>
</ul>

<p>Making Your Pipelines Reusable and Maintainable</p>

<p>Create New Data Sources and Sinks</p>

<ul>
	<li>Apache Beam Source and Sink API</li>
</ul>

<p>Integrating Apache Beam with other Big Data Systems</p>

<ul>
	<li>Apache Hadoop, Apache Spark, Apache Kafka</li>
</ul>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 14
                                            [status] => published
                                            [changed] => 1700037430
                                            [source_title] => Unified Batch and Stream Processing with Apache Beam
                                            [source_language] => en
                                            [cert_code] => 
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                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => beam
                                        )

                                    [apex] => stdClass Object
                                        (
                                            [course_code] => apex
                                            [hr_nid] => 209525
                                            [title] => Apache Apex: Processing Big Data-in-Motion
                                            [requirements] => <ul>
	<li>An understanding of big data concepts</li>
	<li>An understanding of Java</li>
	<li>Experience with Hadoop</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Enterprise architects</li>
</ul>

                                            [overview] => <p>Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable.</p>

<p>This instructor-led, live training introduces Apache Apex&#39;s unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.</li>
	<li>Build, scale and optimize an Apex application</li>
	<li>Process real-time data streams reliably and with minimum latency</li>
	<li>Use Apex Core and the Apex Malhar library to enable rapid application development</li>
	<li>Use the Apex API to write and re-use existing Java code</li>
	<li>Integrate Apex into other applications as a processing engine</li>
	<li>Tune, test and scale Apex applications</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>To request a customized course outline for this training, please contact us.</p>

<p>&nbsp;</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1700037320
                                            [source_title] => Apache Apex: Processing Big Data-in-Motion
                                            [source_language] => en
                                            [cert_code] => 
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                                                )

                                            [cc] => apex
                                        )

                                    [storm] => stdClass Object
                                        (
                                            [course_code] => storm
                                            [hr_nid] => 208253
                                            [title] => Apache Storm
                                            [requirements] => <ul>
	<li>Java development experience</li>
	<li>An understanding of Hadoop concepts</li>
</ul>

                                            [overview] => <p>Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing).</p>

<p>&quot;Storm is for real-time processing what Hadoop is for batch processing!&quot;</p>

<p>In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time.</p>

<p>Some of the topics included in this training include:</p>

<ul>
	<li>Apache Storm in the context of Hadoop</li>
	<li>Working with unbounded data</li>
	<li>Continuous computation</li>
	<li>Real-time analytics</li>
	<li>Distributed RPC and ETL processing</li>
</ul>

<p>Request this course now!</p>

<p><strong>Audience</strong></p>

<ul>
	<li>Software and ETL developers</li>
	<li>Mainframe professionals</li>
	<li>Data scientists</li>
	<li>Big data analysts</li>
	<li>Hadoop professionals</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>&nbsp;&nbsp; &nbsp;Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>Request a customized course outline for this training!</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1700037303
                                            [source_title] => Apache Storm
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => storm
                                        )

                                    [nifi] => stdClass Object
                                        (
                                            [course_code] => nifi
                                            [hr_nid] => 212800
                                            [title] => Apache NiFi for Administrators
                                            [requirements] => <ul>
	<li>Experience with Linux command line.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>System administrators</li>
	<li>Data engineers</li>
	<li>Developers</li>
	<li>DevOps</li>
</ul>

                                            [overview] => <p>Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time.</p>

<p>In this instructor-led, live training (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apachi NiFi.</li>
	<li>Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.</li>
	<li>Automate dataflows.</li>
	<li>Enable streaming analytics.</li>
	<li>Apply various approaches for data ingestion.</li>
	<li>Transform Big Data and into business insights.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt; (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apachi NiFi.</li>
	<li>Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.</li>
	<li>Automate dataflows.</li>
	<li>Enable streaming analytics.</li>
	<li>Apply various approaches for data ingestion.</li>
	<li>Transform Big Data and into business insights.</li>
</ul>

                                            [outline] => <p>Introduction to Apache NiFi&nbsp;&nbsp;&nbsp;</p>

<ul>
	<li>Data at rest vs data in motion</li>
</ul>

<p>Overview of Big Data and Apache Hadoop</p>

<ul>
	<li>HDFS and MapReduce architecture</li>
</ul>

<p>Setting up and Running a NiFi Cluster</p>

<ul>
	<li>Cluster Integration</li>
	<li>Load Balancing/Redundancy</li>
	<li>Mass Orchestration of NiFi (via Ansible)</li>
</ul>

<p>NiFi Operations</p>

<ul>
	<li>Database Aggregating, Splitting and Transforming</li>
	<li>Data Extractions, Logging, etc.</li>
	<li>Integrating with Splunk (optional)</li>
</ul>

<p>Monitoring and Recovery</p>

<ul>
	<li>Recovering without Data Loss</li>
	<li>Autonomous Recovery</li>
</ul>

<p>Optimizing NiFI</p>

<ul>
	<li>Performance tuning</li>
	<li>Optimizing Nifi Setup</li>
</ul>

<p>Best practices</p>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1700037360
                                            [source_title] => Apache NiFi for Administrators
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
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                                                )

                                            [cc] => nifi
                                        )

                                    [nifidev] => stdClass Object
                                        (
                                            [course_code] => nifidev
                                            [hr_nid] => 212804
                                            [title] => Apache NiFi for Developers
                                            [requirements] => <ul>
	<li>Java programming experience.</li>
	<li>Experience with Maven.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Data engineers</li>
</ul>

                                            [overview] => <p>Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time.</p>

<p>In this instructor-led, live training, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand NiFi&#39;s architecture and dataflow concepts.</li>
	<li>Develop extensions using NiFi and third-party APIs.</li>
	<li>Custom develop their own Apache Nifi processor.</li>
	<li>Ingest and process real-time data from disparate and uncommon file formats and data sources.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand NiFi&#39;s architecture and dataflow concepts.</li>
	<li>Develop extensions using NiFi and third-party APIs.</li>
	<li>Custom develop their own Apache Nifi processor.</li>
	<li>Ingest and process real-time data from disparate and uncommon file formats and data sources.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<ul>
	<li>Data at rest vs data in motion</li>
</ul>

<p>Overview of Big Data Tools and Technologies</p>

<ul>
	<li>Hadoop (HDFS and MapReduce) and Spark</li>
</ul>

<p>Installing and Configuring NiFi</p>

<p>Overview of NiFi Architecture</p>

<p>Development Approaches</p>

<ul>
	<li>Application development tools and mindset</li>
	<li>Extract, Transform, and Load (ETL) tools and mindset</li>
</ul>

<p>Design Considerations</p>

<p>Components, Events, and Processor Patterns</p>

<p>Exercise: Streaming Data Feeds into HDFS</p>

<p>Error Handling</p>

<p>Controller Services</p>

<p>Exercise: Ingesting Data from IoT Devices using Web-Based APIs</p>

<p>Exercise: Developing a Custom Apache Nifi Processor using JSON</p>

<p>Testing and Troubleshooting</p>

<p>Contributing to Apache NiFi</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037361
                                            [source_title] => Apache NiFi for Developers
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
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                                                    [field_overview] => 
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                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => nifidev
                                        )

                                    [flink] => stdClass Object
                                        (
                                            [course_code] => flink
                                            [hr_nid] => 209489
                                            [title] => Apache Flink Fundamentals
                                            [requirements] => <ul>
	<li>Experience with Java or Scala</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Architects</li>
	<li>Data engineers</li>
	<li>Analytics professionals</li>
	<li>Technical managers</li>
</ul>

                                            [overview] => <p>Apache Flink is an open-source framework for scalable stream and batch data processing.</p>

<p>This instructor-led, live training&nbsp;(online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Set up an environment for developing data analysis applications.</li>
	<li>Understand how Apache Flink&#39;s&nbsp;graph-processing library (Gelly) works.</li>
	<li>Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.</li>
	<li>Manage diverse workloads.</li>
	<li>Perform advanced analytics.</li>
	<li>Set up a multi-node Flink cluster.</li>
	<li>Measure and optimize performance.</li>
	<li>Integrate Flink with different Big Data systems.</li>
	<li>Compare Flink capabilities with those of other big data processing frameworks.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt;&nbsp;(online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Set up an environment for developing data analysis applications.</li>
	<li>Understand how Apache Flink&#39;s&nbsp;graph-processing library (Gelly) works.</li>
	<li>Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.</li>
	<li>Manage diverse workloads.</li>
	<li>Perform advanced analytics.</li>
	<li>Set up a multi-node Flink cluster.</li>
	<li>Measure and optimize performance.</li>
	<li>Integrate Flink with different Big Data systems.</li>
	<li>Compare Flink capabilities with those of other big data processing frameworks.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Installing and Configuring Apache Flink</p>

<p>Overview of Flink Architecture</p>

<p>Developing Data Streaming Applications in Flink</p>

<p>Managing Diverse Workloads</p>

<p>Performing Advanced Analytics</p>

<p>Setting up a Multi-Node Flink Cluster</p>

<p>Mastering Flink DataStream API</p>

<p>Understanding Flink Libraries</p>

<p>Integrating Flink with Other Big Data Tools</p>

<p>Testing and Troubleshooting</p>

<p>Summary and Next Steps</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1700037319
                                            [source_title] => Apache Flink Fundamentals
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
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                                                )

                                            [cc] => flink
                                        )

                                    [sparkpython] => stdClass Object
                                        (
                                            [course_code] => sparkpython
                                            [hr_nid] => 279430
                                            [title] => Python and Spark for Big Data (PySpark)
                                            [requirements] => <ul>
	<li>General programming skills</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>IT Professionals</li>
	<li>Data Scientists</li>
</ul>

                                            [overview] => <p>Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.</p>

<p>In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Learn how to use Spark with Python to analyze Big Data.</li>
	<li>Work on exercises that mimic real world cases.</li>
	<li>Use different tools and techniques for big data analysis using PySpark.</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Learn how to use Spark with Python to analyze Big Data.</li>
	<li>Work on exercises that mimic real world cases.</li>
	<li>Use different tools and techniques for big data analysis using PySpark.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Understanding Big Data</p>

<p>Overview of Spark</p>

<p>Overview of Python</p>

<p>Overview of PySpark</p>

<ul>
	<li>Distributing Data Using Resilient Distributed Datasets Framework</li>
	<li>Distributing Computation Using Spark API Operators</li>
</ul>

<p>Setting Up Python with Spark</p>

<p>Setting Up PySpark</p>

<p>Using Amazon Web Services (AWS) EC2 Instances for Spark</p>

<p>Setting Up Databricks</p>

<p>Setting Up the AWS EMR Cluster</p>

<p>Learning the Basics of Python Programming</p>

<ul>
	<li>Getting Started with Python</li>
	<li>Using the Jupyter Notebook</li>
	<li>Using Variables and Simple Data Types</li>
	<li>Working with Lists</li>
	<li>Using if Statements</li>
	<li>Using User Inputs</li>
	<li>Working with while Loops</li>
	<li>Implementing Functions</li>
	<li>Working with Classes</li>
	<li>Working with Files and Exceptions</li>
	<li>Working with Projects, Data, and APIs</li>
</ul>

<p>Learning the Basics of Spark DataFrame</p>

<ul>
	<li>Getting Started with Spark DataFrames</li>
	<li>Implementing Basic Operations with Spark</li>
	<li>Using Groupby and Aggregate Operations</li>
	<li>Working with Timestamps and Dates</li>
</ul>

<p>Working on a Spark DataFrame Project Exercise</p>

<p>Understanding Machine Learning with MLlib</p>

<p>Working with MLlib, Spark, and Python for Machine Learning</p>

<p>Understanding Regressions</p>

<ul>
	<li>Learning Linear Regression Theory</li>
	<li>Implementing a Regression Evaluation Code</li>
	<li>Working on a Sample Linear Regression Exercise</li>
	<li>Learning Logistic Regression Theory</li>
	<li>Implementing a Logistic Regression Code</li>
	<li>Working on a Sample Logistic Regression Exercise</li>
</ul>

<p>Understanding Random Forests and Decision Trees</p>

<ul>
	<li>Learning Tree Methods Theory</li>
	<li>Implementing Decision Trees and Random Forest Codes</li>
	<li>Working on a Sample Random Forest Classification Exercise</li>
</ul>

<p>Working with K-means Clustering</p>

<ul>
	<li>Understanding K-means Clustering Theory</li>
	<li>Implementing a K-means Clustering Code</li>
	<li>Working on a Sample Clustering Exercise</li>
</ul>

<p>Working with Recommender Systems</p>

<p>Implementing Natural Language Processing</p>

<ul>
	<li>Understanding Natural Language Processing (NLP)</li>
	<li>Overview of NLP Tools</li>
	<li>Working on a Sample NLP Exercise</li>
</ul>

<p>Streaming with Spark on Python</p>

<ul>
	<li>Overview Streaming with Spark</li>
	<li>Sample Spark Streaming Exercise</li>
</ul>

<p>Closing Remarks</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1715349940
                                            [source_title] => Python and Spark for Big Data (PySpark)
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => -998
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
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                                                    [field_overview] => 
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                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => sparkpython
                                        )

                                    [graphcomputing] => stdClass Object
                                        (
                                            [course_code] => graphcomputing
                                            [hr_nid] => 278402
                                            [title] => Introduction to Graph Computing
                                            [requirements] => <ul>
	<li>An undersanding of Java programming and frameworks</li>
	<li>A general understanding of Python is helpful but not required</li>
	<li>A general understanding of database concepts</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Many real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).</p>

<p>In this instructor-led, live training, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics and Distributed Graph Processing) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand how graph data is persisted and traversed.</li>
	<li>Select the best framework for a given task (from graph databases to batch processing frameworks.)</li>
	<li>Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.</li>
	<li>View real-world big data problems in terms of graphs, processes and traversals.</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand how graph data is persisted and traversed.</li>
	<li>Select the best framework for a given task (from graph databases to batch processing frameworks.)</li>
	<li>Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.</li>
	<li>View real-world big data problems in terms of graphs, processes and traversals.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<ul>
	<li>Graph databases and libraries</li>
</ul>

<p>Understanding Graph Data</p>

<ul>
	<li>The graph as a data structure</li>
	<li>Using vertices (dots) and edges (lines) to model real-world scenarios</li>
</ul>

<p>Using Graph Databases to Model, Persist and Process Graph Data</p>

<ul>
	<li>Local graph algorithms/traversals</li>
	<li>neo4j, OrientDB and Titan</li>
</ul>

<p>Exercise: Modeling Graph Data with neo4j</p>

<ul>
	<li>Whiteboard data modeling</li>
</ul>

<p>Beyond Graph Databases: Graph Computing</p>

<ul>
	<li>Understanding the property graph</li>
	<li>Graph modeling different scenarios (software graph, discussion graph, concept graph)</li>
</ul>

<p>Solving Real-World Problems with Traversals</p>

<ul>
	<li>Algorithmic/directed walk over the graph</li>
	<li>Determining circular cependencies</li>
</ul>

<p>Case Study: Ranking Discussion Contributors</p>

<ul>
	<li>Ranking by number and depth of contributed discussions</li>
	<li>A note on sentiment and concept analysis</li>
</ul>

<p>Graph Computing: Local, In-Memory Graph toolkits</p>

<ul>
	<li>Graph analysis and visualization</li>
	<li>JUNG, NetworkX, and iGraph</li>
</ul>

<p>Exercise: Modeling Graph Data with NetworkX</p>

<ul>
	<li>Using NetworkX to model a complex system</li>
</ul>

<p>Graph Computing: Batch Processing Graph Frameworks</p>

<ul>
	<li>Leveraging Hadoop for storage (HDFS) and processing (MapReduce)</li>
	<li>Overview of iterative algorithms</li>
	<li>Hama, Giraph, and GraphLab</li>
</ul>

<p>Graph Computing: Graph-Parallel Computation</p>

<ul>
	<li>Unifying ETL, exploratory analysis, and iterative graph computation within a single system</li>
	<li>GraphX</li>
</ul>

<p>Setup and Installation</p>

<ul>
	<li>Hadoop and Spark</li>
</ul>

<p>GraphX Operators</p>

<ul>
	<li>Property, structural, join, neighborhood aggregation, caching and uncaching</li>
</ul>

<p>Iterating with Pregel API</p>

<ul>
	<li>Passing arguments for sending, receiving and computing</li>
</ul>

<p>Building a Graph</p>

<ul>
	<li>Using vertices and edges in an RDD or on disk</li>
</ul>

<p>Designing Scalable Algorithms</p>

<ul>
	<li>GraphX Optimization</li>
</ul>

<p>Accessing Additional Algorithms</p>

<ul>
	<li>PageRank, Connected Components, Triangle Counting</li>
</ul>

<p>Exercis: Page Rank and Top Users</p>

<ul>
	<li>Building and processing graph data using text files as input</li>
</ul>

<p>Deploying to Production</p>

<p>Closing Remarks</p>

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                                            [title] => Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
                                            [requirements] => 
                                            [overview] => <p>This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.</p>

                                            [category_overview] => 
                                            [outline] => <ol>
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	<ol>
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		<li>RNN long-term memory (TSTM) architecture</li>
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                                            [language] => en
                                            [duration] => 21
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                                            [requirements] => <p>Knowledge of one of the following:</p>

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                                            [overview] => <p>MLlib is Spark&rsquo;s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.</p>

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<p><strong>Audience</strong></p>

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                                            [category_overview] => 
                                            [outline] => <h2>spark.mllib: data types, algorithms, and utilities</h2>

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		<li>association rules</li>
		<li>PrefixSpan</li>
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	</li>
	<li>Evaluation metrics</li>
	<li>PMML model export</li>
	<li>Optimization (developer)
	<ul>
		<li>stochastic gradient descent</li>
		<li>limited-memory BFGS (L-BFGS)</li>
	</ul>
	</li>
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<h2>spark.ml: high-level APIs for ML pipelines</h2>

<ul>
	<li>Overview: estimators, transformers and pipelines</li>
	<li>Extracting, transforming and selecting features</li>
	<li>Classification and regression</li>
	<li>Clustering</li>
	<li>Advanced topics</li>
</ul>

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                            [title] => Programming with Big Data in R
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                            [overview] => <p>Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.</p>

                            [category_overview] => 
                            [outline] => <h3>Introduction to Programming Big Data with R (bpdR)</h3>

<ul>
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<h3>Statistics Applications</h3>

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                                            [hr_nid] => 212656
                                            [title] => Introduction to Data Visualization with Tidyverse and R
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                                            [overview] => <p>The Tidyverse is a collection of versatile R packages for cleaning, processing, modeling, and visualizing data. Some of the packages included are: ggplot2, dplyr, tidyr, readr, purrr, and tibble.</p>

<p>In this instructor-led, live training, participants will learn how to manipulate and visualize data using the tools included in the Tidyverse.</p>

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<p><strong>Format of the course</strong></p>

<ul>
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                                            [category_overview] => 
                                            [outline] => <p>Introduction</p>

<ul>
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<p>Preparing the dataset</p>

<p>Importing and filtering data</p>

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<p>Visualizing the data (graphs, scatter plots)</p>

<p>Grouping and summarizing the data</p>

<p>Visualizing the data (line plots, bar plots, histograms, boxplots)</p>

<p>Working with non-standard data</p>

<p>Closing remarks</p>

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                                            [source_title] => Introduction to Data Visualization with Tidyverse and R
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<p><strong>Audience</strong></p>

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                                            [overview] => <p>Data Vault Modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or &quot;all the data, all the time&quot;. Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema.</p>

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                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn how to build a Data Vault.</p>

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                                            [outline] => <p>Introduction</p>

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<p>Query techniques</p>

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<p>Loading Link Entities</p>

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<p>Using SEI/CMM Level 5 templates to obtain repeatable, reliable, and quantifiable results</p>

<p>Developing a consistent and repeatable ETL (Extract, Transform, Load) process</p>

<p>Building and deploying highly scalable and repeatable warehouses</p>

<p>Closing remarks</p>

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                                            [course_code] => sparkstreaming
                                            [hr_nid] => 356863
                                            [title] => Spark Streaming with Python and Kafka
                                            [requirements] => <ul>
	<li>Experience with Python and Apache Kafka</li>
	<li>Familiarity with stream-processing platforms</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Data engineers</li>
	<li>Data scientists</li>
	<li>Programmers</li>
</ul>

                                            [overview] => <p>Apache Spark Streaming is a scalable, open source stream&nbsp;processing system that allows users to process real-time data from supported sources. Spark Streaming enables fault-tolerant processing of data streams.</p>

<p>This instructor-led, live training (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.</p>

<p>By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.</p>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Spark Streaming features in processing and analyzing real-time data.</p>

<p>By the end of this training, participants will be able to use Spark Streaming to process live data streams for use in databases, filesystems, and live dashboards.</p>

                                            [outline] => <p>Introduction</p>

<p>Overview of Spark Streaming Features and Architecture</p>

<ul>
	<li>Supported data sources</li>
	<li>Core APIs</li>
</ul>

<p>Preparing the Environment</p>

<ul>
	<li>Dependencies</li>
	<li>Spark and streaming context</li>
	<li>Connecting to Kafka</li>
</ul>

<p>Processing Messages</p>

<ul>
	<li>Parsing inbound messages as JSON</li>
	<li>ETL processes</li>
	<li>Starting the streaming context</li>
</ul>

<p>Performing a Windowed Stream Processing</p>

<ul>
	<li>Slide interval</li>
	<li>Checkpoint delivery configuration</li>
	<li>Launching the environment</li>
</ul>

<p>Prototyping the Processing Code</p>

<ul>
	<li>Connecting to a Kafka topic</li>
	<li>Retrieving JSON from data source using Paw</li>
	<li>Variations and additional processing</li>
</ul>

<p>Streaming the Code</p>

<ul>
	<li>Job control variables</li>
	<li>Defining values to match</li>
	<li>Functions and conditions</li>
</ul>

<p>Acquiring Stream Output</p>

<ul>
	<li>Counters</li>
	<li>Kafka output (matched and non-matched)</li>
</ul>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037710
                                            [source_title] => Spark Streaming with Python and Kafka
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => sparkstreaming
                                        )

                                    [ksql] => stdClass Object
                                        (
                                            [course_code] => ksql
                                            [hr_nid] => 318463
                                            [title] => Confluent KSQL
                                            [requirements] => <ul>
	<li>Experience with Apache Kafka.</li>
	<li>Java programming experience.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Confluent KSQL is a stream processing framework built on top of Apache Kafka. It enables real-time data processing using SQL operations.</p>

<p>This instructor-led, live training (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Confluent KSQL.</li>
	<li>Set up a stream processing pipeline using only SQL commands (no Java or Python coding).</li>
	<li>Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.</li>
	<li>Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at developers who wish to implement Apache Kafka stream processing without writing code.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Confluent KSQL.</li>
	<li>Set up a stream processing pipeline using only SQL commands (no Java or Python coding).</li>
	<li>Carry out data filtering, transformations, aggregations, joins, windowing, and sessionization entirely in SQL.</li>
	<li>Design and deploy interactive, continuous queries for streaming ETL and real-time analytics.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Setting up Confluent KSQL</p>

<p>Overview of KSQL Features and Architecture</p>

<p>How KSQL Interacts with Apache Kafka</p>

<p>Use Cases for KSQL</p>

<p>KSQL Command Line and Operations</p>

<p>Ingesting Data (CSV, JSON, etc.)</p>

<p>Creating a Stream</p>

<p>Creating a Table</p>

<p>Advanced KSQL Operations (Joins, Windowing, Aggregations, Geospatial, etc.)</p>

<p>Deploying KSQL to Production</p>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037528
                                            [source_title] => Confluent KSQL
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => ksql
                                        )

                                    [apacheignite] => stdClass Object
                                        (
                                            [course_code] => apacheignite
                                            [hr_nid] => 209621
                                            [title] => Apache Ignite for Developers
                                            [requirements] => <ul>
	<li>An understanding of databases</li>
	<li>An understanding of Java</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Apache Ignite is an in-memory computing platform that sits between the application and data layer to improve speed, scale, and availability.</p>

<p>This instructor-led, live training (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.</li>
	<li>Achieve persistence without syncing data back to a relational database.</li>
	<li>Use Ignite to carry out SQL and distributed joins.</li>
	<li>Improve performance by moving data closer to the CPU, using RAM as a storage.</li>
	<li>Spread data sets across a cluster to achieve horizontal scalability.</li>
	<li>Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt; (online or onsite) is aimed at developers who wish to learn the principles behind persistent and pure in-memory storage as they step through the creation of a sample in-memory computing project.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Use Ignite for in-memory, on-disk persistence as well as a purely distributed in-memory database.</li>
	<li>Achieve persistence without syncing data back to a relational database.</li>
	<li>Use Ignite to carry out SQL and distributed joins.</li>
	<li>Improve performance by moving data closer to the CPU, using RAM as a storage.</li>
	<li>Spread data sets across a cluster to achieve horizontal scalability.</li>
	<li>Integrate Ignite with RDBMS, NoSQL, Hadoop and machine learning processors.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Overview of Big Data Tools and Technologies</p>

<p>Installing and Configuring Apache Ignite</p>

<p>Overview of Ignite Architecture</p>

<p>Querying Data in Ignite</p>

<p>Spreading Large Data Sets across a Cluster</p>

<p>Understanding the In-Memory Data Grid</p>

<p>Writing a Service in Ignite</p>

<p>Running Distributed Computing with Ignite</p>

<p>Integrating Ignite with RDBMS, NoSQL, Hadoop and Machine Learning Processors</p>

<p>Testing and Troubleshooting</p>

<p>Summary and Next Steps</p>

                                            [language] => en
                                            [duration] => 14
                                            [status] => published
                                            [changed] => 1700037322
                                            [source_title] => Apache Ignite for Developers
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => apacheignite
                                        )

                                    [beam] => stdClass Object
                                        (
                                            [course_code] => beam
                                            [hr_nid] => 283646
                                            [title] => Unified Batch and Stream Processing with Apache Beam
                                            [requirements] => <ul>
	<li>Experience with Python Programming.</li>
	<li>Experience with the Linux command line.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It&#39;s power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam&#39;s supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system.</p>

<p>In this instructor-led, live training (onsite or remote), participants will learn how to implement the Apache Beam SDKs in a Java or Python application that defines a data processing pipeline for decomposing a big data set into smaller chunks for independent, parallel processing.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apache Beam.</li>
	<li>Use a single programming model to carry out both batch and stream processing from withing their Java or Python application.</li>
	<li>Execute pipelines across multiple environments.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

<p><strong>Note</strong></p>

<ul>
	<li>This course will be available Scala in the future. Please contact us to arrange.</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>Introduction</p>

<ul>
	<li>Apache Beam vs MapReduce, Spark Streaming, Kafka Streaming, Storm and Flink</li>
</ul>

<p>Installing and Configuring Apache Beam</p>

<p>Overview of Apache Beam Features and Architecture</p>

<ul>
	<li>Beam Model, SDKs, Beam Pipeline Runners</li>
	<li>Distributed processing back-ends</li>
</ul>

<p>Understanding the Apache Beam Programming Model</p>

<ul>
	<li>How a pipeline is executed</li>
</ul>

<p>Running a sample pipeline</p>

<ul>
	<li>Preparing a WordCount pipeline</li>
	<li>Executing the Pipeline locally</li>
</ul>

<p>Designing a Pipeline</p>

<ul>
	<li>Planning the structure, choosing the transforms, and determining the input and output methods</li>
</ul>

<p>Creating the Pipeline</p>

<ul>
	<li>Writing the driver program and defining the pipeline</li>
	<li>Using Apache Beam classes</li>
	<li>Data sets, transforms, I/O, data encoding, etc.</li>
</ul>

<p>Executing the Pipeline</p>

<ul>
	<li>Executing the pipeline locally, on remote machines, and on a public cloud</li>
	<li>Choosing a runner</li>
	<li>Runner-specific configurations</li>
</ul>

<p>Testing and Debugging Apache Beam</p>

<ul>
	<li>Using type hints to emulate static typing</li>
	<li>Managing Python Pipeline Dependencies</li>
</ul>

<p>Processing Bounded and Unbounded Datasets</p>

<ul>
	<li>Windowing and Triggers</li>
</ul>

<p>Making Your Pipelines Reusable and Maintainable</p>

<p>Create New Data Sources and Sinks</p>

<ul>
	<li>Apache Beam Source and Sink API</li>
</ul>

<p>Integrating Apache Beam with other Big Data Systems</p>

<ul>
	<li>Apache Hadoop, Apache Spark, Apache Kafka</li>
</ul>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 14
                                            [status] => published
                                            [changed] => 1700037430
                                            [source_title] => Unified Batch and Stream Processing with Apache Beam
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => beam
                                        )

                                    [apex] => stdClass Object
                                        (
                                            [course_code] => apex
                                            [hr_nid] => 209525
                                            [title] => Apache Apex: Processing Big Data-in-Motion
                                            [requirements] => <ul>
	<li>An understanding of big data concepts</li>
	<li>An understanding of Java</li>
	<li>Experience with Hadoop</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Enterprise architects</li>
</ul>

                                            [overview] => <p>Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable.</p>

<p>This instructor-led, live training introduces Apache Apex&#39;s unified stream processing architecture, and walks participants through the creation of a distributed application using Apex on Hadoop.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand data processing pipeline concepts such as connectors for sources and sinks, common data transformations, etc.</li>
	<li>Build, scale and optimize an Apex application</li>
	<li>Process real-time data streams reliably and with minimum latency</li>
	<li>Use Apex Core and the Apex Malhar library to enable rapid application development</li>
	<li>Use the Apex API to write and re-use existing Java code</li>
	<li>Integrate Apex into other applications as a processing engine</li>
	<li>Tune, test and scale Apex applications</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>To request a customized course outline for this training, please contact us.</p>

<p>&nbsp;</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1700037320
                                            [source_title] => Apache Apex: Processing Big Data-in-Motion
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => apex
                                        )

                                    [storm] => stdClass Object
                                        (
                                            [course_code] => storm
                                            [hr_nid] => 208253
                                            [title] => Apache Storm
                                            [requirements] => <ul>
	<li>Java development experience</li>
	<li>An understanding of Hadoop concepts</li>
</ul>

                                            [overview] => <p>Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing).</p>

<p>&quot;Storm is for real-time processing what Hadoop is for batch processing!&quot;</p>

<p>In this instructor-led live training, participants will learn how to install and configure Apache Storm, then develop and deploy an Apache Storm application for processing big data in real-time.</p>

<p>Some of the topics included in this training include:</p>

<ul>
	<li>Apache Storm in the context of Hadoop</li>
	<li>Working with unbounded data</li>
	<li>Continuous computation</li>
	<li>Real-time analytics</li>
	<li>Distributed RPC and ETL processing</li>
</ul>

<p>Request this course now!</p>

<p><strong>Audience</strong></p>

<ul>
	<li>Software and ETL developers</li>
	<li>Mainframe professionals</li>
	<li>Data scientists</li>
	<li>Big data analysts</li>
	<li>Hadoop professionals</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>&nbsp;&nbsp; &nbsp;Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => 
                                            [outline] => <p>Request a customized course outline for this training!</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1700037303
                                            [source_title] => Apache Storm
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => storm
                                        )

                                    [nifi] => stdClass Object
                                        (
                                            [course_code] => nifi
                                            [hr_nid] => 212800
                                            [title] => Apache NiFi for Administrators
                                            [requirements] => <ul>
	<li>Experience with Linux command line.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>System administrators</li>
	<li>Data engineers</li>
	<li>Developers</li>
	<li>DevOps</li>
</ul>

                                            [overview] => <p>Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time.</p>

<p>In this instructor-led, live training (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apachi NiFi.</li>
	<li>Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.</li>
	<li>Automate dataflows.</li>
	<li>Enable streaming analytics.</li>
	<li>Apply various approaches for data ingestion.</li>
	<li>Transform Big Data and into business insights.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt; (onsite or remote), participants will learn how to deploy and manage Apache NiFi in a live lab environment.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Install and configure Apachi NiFi.</li>
	<li>Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.</li>
	<li>Automate dataflows.</li>
	<li>Enable streaming analytics.</li>
	<li>Apply various approaches for data ingestion.</li>
	<li>Transform Big Data and into business insights.</li>
</ul>

                                            [outline] => <p>Introduction to Apache NiFi&nbsp;&nbsp;&nbsp;</p>

<ul>
	<li>Data at rest vs data in motion</li>
</ul>

<p>Overview of Big Data and Apache Hadoop</p>

<ul>
	<li>HDFS and MapReduce architecture</li>
</ul>

<p>Setting up and Running a NiFi Cluster</p>

<ul>
	<li>Cluster Integration</li>
	<li>Load Balancing/Redundancy</li>
	<li>Mass Orchestration of NiFi (via Ansible)</li>
</ul>

<p>NiFi Operations</p>

<ul>
	<li>Database Aggregating, Splitting and Transforming</li>
	<li>Data Extractions, Logging, etc.</li>
	<li>Integrating with Splunk (optional)</li>
</ul>

<p>Monitoring and Recovery</p>

<ul>
	<li>Recovering without Data Loss</li>
	<li>Autonomous Recovery</li>
</ul>

<p>Optimizing NiFI</p>

<ul>
	<li>Performance tuning</li>
	<li>Optimizing Nifi Setup</li>
</ul>

<p>Best practices</p>

<p>Troubleshooting</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1700037360
                                            [source_title] => Apache NiFi for Administrators
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
                                            [excluded_sites] => 
                                            [use_mt] => stdClass Object
                                                (
                                                    [field_overview] => 
                                                    [field_course_outline] => 
                                                    [field_prerequisits] => 
                                                    [field_overview_in_category] => 
                                                )

                                            [cc] => nifi
                                        )

                                    [nifidev] => stdClass Object
                                        (
                                            [course_code] => nifidev
                                            [hr_nid] => 212804
                                            [title] => Apache NiFi for Developers
                                            [requirements] => <ul>
	<li>Java programming experience.</li>
	<li>Experience with Maven.</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Data engineers</li>
</ul>

                                            [overview] => <p>Apache NiFi (Hortonworks DataFlow) is a real-time integrated data logistics and simple event processing platform that enables the moving, tracking and automation of data between systems. It is written using flow-based programming and provides a web-based user interface to manage dataflows in real time.</p>

<p>In this instructor-led, live training, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand NiFi&#39;s architecture and dataflow concepts.</li>
	<li>Develop extensions using NiFi and third-party APIs.</li>
	<li>Custom develop their own Apache Nifi processor.</li>
	<li>Ingest and process real-time data from disparate and uncommon file formats and data sources.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand NiFi&#39;s architecture and dataflow concepts.</li>
	<li>Develop extensions using NiFi and third-party APIs.</li>
	<li>Custom develop their own Apache Nifi processor.</li>
	<li>Ingest and process real-time data from disparate and uncommon file formats and data sources.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<ul>
	<li>Data at rest vs data in motion</li>
</ul>

<p>Overview of Big Data Tools and Technologies</p>

<ul>
	<li>Hadoop (HDFS and MapReduce) and Spark</li>
</ul>

<p>Installing and Configuring NiFi</p>

<p>Overview of NiFi Architecture</p>

<p>Development Approaches</p>

<ul>
	<li>Application development tools and mindset</li>
	<li>Extract, Transform, and Load (ETL) tools and mindset</li>
</ul>

<p>Design Considerations</p>

<p>Components, Events, and Processor Patterns</p>

<p>Exercise: Streaming Data Feeds into HDFS</p>

<p>Error Handling</p>

<p>Controller Services</p>

<p>Exercise: Ingesting Data from IoT Devices using Web-Based APIs</p>

<p>Exercise: Developing a Custom Apache Nifi Processor using JSON</p>

<p>Testing and Troubleshooting</p>

<p>Contributing to Apache NiFi</p>

<p>Summary and Conclusion</p>

                                            [language] => en
                                            [duration] => 7
                                            [status] => published
                                            [changed] => 1700037361
                                            [source_title] => Apache NiFi for Developers
                                            [source_language] => en
                                            [cert_code] => 
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                                            [cc] => nifidev
                                        )

                                    [flink] => stdClass Object
                                        (
                                            [course_code] => flink
                                            [hr_nid] => 209489
                                            [title] => Apache Flink Fundamentals
                                            [requirements] => <ul>
	<li>Experience with Java or Scala</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>Architects</li>
	<li>Data engineers</li>
	<li>Analytics professionals</li>
	<li>Technical managers</li>
</ul>

                                            [overview] => <p>Apache Flink is an open-source framework for scalable stream and batch data processing.</p>

<p>This instructor-led, live training&nbsp;(online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Set up an environment for developing data analysis applications.</li>
	<li>Understand how Apache Flink&#39;s&nbsp;graph-processing library (Gelly) works.</li>
	<li>Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.</li>
	<li>Manage diverse workloads.</li>
	<li>Perform advanced analytics.</li>
	<li>Set up a multi-node Flink cluster.</li>
	<li>Measure and optimize performance.</li>
	<li>Integrate Flink with different Big Data systems.</li>
	<li>Compare Flink capabilities with those of other big data processing frameworks.</li>
</ul>

<p><strong>Format of the Course</strong></p>

<ul>
	<li>Interactive lecture and discussion.</li>
	<li>Lots of exercises and practice.</li>
	<li>Hands-on implementation in a live-lab environment.</li>
</ul>

<p><strong>Course Customization Options</strong></p>

<ul>
	<li>To request a customized training for this course, please contact us to arrange.</li>
</ul>

                                            [category_overview] => <p>This instructor-led, live training in &lt;loc&gt;&nbsp;(online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Set up an environment for developing data analysis applications.</li>
	<li>Understand how Apache Flink&#39;s&nbsp;graph-processing library (Gelly) works.</li>
	<li>Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.</li>
	<li>Manage diverse workloads.</li>
	<li>Perform advanced analytics.</li>
	<li>Set up a multi-node Flink cluster.</li>
	<li>Measure and optimize performance.</li>
	<li>Integrate Flink with different Big Data systems.</li>
	<li>Compare Flink capabilities with those of other big data processing frameworks.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Installing and Configuring Apache Flink</p>

<p>Overview of Flink Architecture</p>

<p>Developing Data Streaming Applications in Flink</p>

<p>Managing Diverse Workloads</p>

<p>Performing Advanced Analytics</p>

<p>Setting up a Multi-Node Flink Cluster</p>

<p>Mastering Flink DataStream API</p>

<p>Understanding Flink Libraries</p>

<p>Integrating Flink with Other Big Data Tools</p>

<p>Testing and Troubleshooting</p>

<p>Summary and Next Steps</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1700037319
                                            [source_title] => Apache Flink Fundamentals
                                            [source_language] => en
                                            [cert_code] => 
                                            [weight] => 0
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                                            [cc] => flink
                                        )

                                    [sparkpython] => stdClass Object
                                        (
                                            [course_code] => sparkpython
                                            [hr_nid] => 279430
                                            [title] => Python and Spark for Big Data (PySpark)
                                            [requirements] => <ul>
	<li>General programming skills</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
	<li>IT Professionals</li>
	<li>Data Scientists</li>
</ul>

                                            [overview] => <p>Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python.</p>

<p>In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Learn how to use Spark with Python to analyze Big Data.</li>
	<li>Work on exercises that mimic real world cases.</li>
	<li>Use different tools and techniques for big data analysis using PySpark.</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Learn how to use Spark with Python to analyze Big Data.</li>
	<li>Work on exercises that mimic real world cases.</li>
	<li>Use different tools and techniques for big data analysis using PySpark.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<p>Understanding Big Data</p>

<p>Overview of Spark</p>

<p>Overview of Python</p>

<p>Overview of PySpark</p>

<ul>
	<li>Distributing Data Using Resilient Distributed Datasets Framework</li>
	<li>Distributing Computation Using Spark API Operators</li>
</ul>

<p>Setting Up Python with Spark</p>

<p>Setting Up PySpark</p>

<p>Using Amazon Web Services (AWS) EC2 Instances for Spark</p>

<p>Setting Up Databricks</p>

<p>Setting Up the AWS EMR Cluster</p>

<p>Learning the Basics of Python Programming</p>

<ul>
	<li>Getting Started with Python</li>
	<li>Using the Jupyter Notebook</li>
	<li>Using Variables and Simple Data Types</li>
	<li>Working with Lists</li>
	<li>Using if Statements</li>
	<li>Using User Inputs</li>
	<li>Working with while Loops</li>
	<li>Implementing Functions</li>
	<li>Working with Classes</li>
	<li>Working with Files and Exceptions</li>
	<li>Working with Projects, Data, and APIs</li>
</ul>

<p>Learning the Basics of Spark DataFrame</p>

<ul>
	<li>Getting Started with Spark DataFrames</li>
	<li>Implementing Basic Operations with Spark</li>
	<li>Using Groupby and Aggregate Operations</li>
	<li>Working with Timestamps and Dates</li>
</ul>

<p>Working on a Spark DataFrame Project Exercise</p>

<p>Understanding Machine Learning with MLlib</p>

<p>Working with MLlib, Spark, and Python for Machine Learning</p>

<p>Understanding Regressions</p>

<ul>
	<li>Learning Linear Regression Theory</li>
	<li>Implementing a Regression Evaluation Code</li>
	<li>Working on a Sample Linear Regression Exercise</li>
	<li>Learning Logistic Regression Theory</li>
	<li>Implementing a Logistic Regression Code</li>
	<li>Working on a Sample Logistic Regression Exercise</li>
</ul>

<p>Understanding Random Forests and Decision Trees</p>

<ul>
	<li>Learning Tree Methods Theory</li>
	<li>Implementing Decision Trees and Random Forest Codes</li>
	<li>Working on a Sample Random Forest Classification Exercise</li>
</ul>

<p>Working with K-means Clustering</p>

<ul>
	<li>Understanding K-means Clustering Theory</li>
	<li>Implementing a K-means Clustering Code</li>
	<li>Working on a Sample Clustering Exercise</li>
</ul>

<p>Working with Recommender Systems</p>

<p>Implementing Natural Language Processing</p>

<ul>
	<li>Understanding Natural Language Processing (NLP)</li>
	<li>Overview of NLP Tools</li>
	<li>Working on a Sample NLP Exercise</li>
</ul>

<p>Streaming with Spark on Python</p>

<ul>
	<li>Overview Streaming with Spark</li>
	<li>Sample Spark Streaming Exercise</li>
</ul>

<p>Closing Remarks</p>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1715349940
                                            [source_title] => Python and Spark for Big Data (PySpark)
                                            [source_language] => en
                                            [cert_code] => 
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                                            [use_mt] => stdClass Object
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                                            [cc] => sparkpython
                                        )

                                    [graphcomputing] => stdClass Object
                                        (
                                            [course_code] => graphcomputing
                                            [hr_nid] => 278402
                                            [title] => Introduction to Graph Computing
                                            [requirements] => <ul>
	<li>An undersanding of Java programming and frameworks</li>
	<li>A general understanding of Python is helpful but not required</li>
	<li>A general understanding of database concepts</li>
</ul>

<p><strong>Audience</strong></p>

<ul>
	<li>Developers</li>
</ul>

                                            [overview] => <p>Many real world problems can be described in terms of graphs. For example, the Web graph, the social network graph, the train network graph and the language graph. These graphs tend to be extremely large; processing them requires a specialized set of tools and processes -- these tools and processes can be referred to as Graph Computing (also known as Graph Analytics).</p>

<p>In this instructor-led, live training, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics and Distributed Graph Processing) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand how graph data is persisted and traversed.</li>
	<li>Select the best framework for a given task (from graph databases to batch processing frameworks.)</li>
	<li>Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.</li>
	<li>View real-world big data problems in terms of graphs, processes and traversals.</li>
</ul>

<p><strong>Format of the course</strong></p>

<ul>
	<li>Part lecture, part discussion, exercises and heavy hands-on practice</li>
</ul>

                                            [category_overview] => <p>In this instructor-led, live training in &lt;loc&gt;, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand how graph data is persisted and traversed.</li>
	<li>Select the best framework for a given task (from graph databases to batch processing frameworks.)</li>
	<li>Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.</li>
	<li>View real-world big data problems in terms of graphs, processes and traversals.</li>
</ul>

                                            [outline] => <p>Introduction</p>

<ul>
	<li>Graph databases and libraries</li>
</ul>

<p>Understanding Graph Data</p>

<ul>
	<li>The graph as a data structure</li>
	<li>Using vertices (dots) and edges (lines) to model real-world scenarios</li>
</ul>

<p>Using Graph Databases to Model, Persist and Process Graph Data</p>

<ul>
	<li>Local graph algorithms/traversals</li>
	<li>neo4j, OrientDB and Titan</li>
</ul>

<p>Exercise: Modeling Graph Data with neo4j</p>

<ul>
	<li>Whiteboard data modeling</li>
</ul>

<p>Beyond Graph Databases: Graph Computing</p>

<ul>
	<li>Understanding the property graph</li>
	<li>Graph modeling different scenarios (software graph, discussion graph, concept graph)</li>
</ul>

<p>Solving Real-World Problems with Traversals</p>

<ul>
	<li>Algorithmic/directed walk over the graph</li>
	<li>Determining circular cependencies</li>
</ul>

<p>Case Study: Ranking Discussion Contributors</p>

<ul>
	<li>Ranking by number and depth of contributed discussions</li>
	<li>A note on sentiment and concept analysis</li>
</ul>

<p>Graph Computing: Local, In-Memory Graph toolkits</p>

<ul>
	<li>Graph analysis and visualization</li>
	<li>JUNG, NetworkX, and iGraph</li>
</ul>

<p>Exercise: Modeling Graph Data with NetworkX</p>

<ul>
	<li>Using NetworkX to model a complex system</li>
</ul>

<p>Graph Computing: Batch Processing Graph Frameworks</p>

<ul>
	<li>Leveraging Hadoop for storage (HDFS) and processing (MapReduce)</li>
	<li>Overview of iterative algorithms</li>
	<li>Hama, Giraph, and GraphLab</li>
</ul>

<p>Graph Computing: Graph-Parallel Computation</p>

<ul>
	<li>Unifying ETL, exploratory analysis, and iterative graph computation within a single system</li>
	<li>GraphX</li>
</ul>

<p>Setup and Installation</p>

<ul>
	<li>Hadoop and Spark</li>
</ul>

<p>GraphX Operators</p>

<ul>
	<li>Property, structural, join, neighborhood aggregation, caching and uncaching</li>
</ul>

<p>Iterating with Pregel API</p>

<ul>
	<li>Passing arguments for sending, receiving and computing</li>
</ul>

<p>Building a Graph</p>

<ul>
	<li>Using vertices and edges in an RDD or on disk</li>
</ul>

<p>Designing Scalable Algorithms</p>

<ul>
	<li>GraphX Optimization</li>
</ul>

<p>Accessing Additional Algorithms</p>

<ul>
	<li>PageRank, Connected Components, Triangle Counting</li>
</ul>

<p>Exercis: Page Rank and Top Users</p>

<ul>
	<li>Building and processing graph data using text files as input</li>
</ul>

<p>Deploying to Production</p>

<p>Closing Remarks</p>

                                            [language] => en
                                            [duration] => 28
                                            [status] => published
                                            [changed] => 1715349940
                                            [source_title] => Introduction to Graph Computing
                                            [source_language] => en
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                                            [cc] => graphcomputing
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                                    [aitech] => stdClass Object
                                        (
                                            [course_code] => aitech
                                            [hr_nid] => 199320
                                            [title] => Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
                                            [requirements] => 
                                            [overview] => <p>This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.</p>

                                            [category_overview] => 
                                            [outline] => <ol>
	<li>Distribution big data
	<ol>
		<li>Data mining methods (training single systems + distributed prediction: traditional machine learning algorithms + Mapreduce distributed prediction)</li>
		<li>Apache Spark MLlib</li>
	</ol>
	</li>
	<li>Recommendations and Advertising:
	<ol>
		<li>Natural language</li>
		<li>Text clustering, text categorization (labeling), synonyms</li>
		<li>User profile restore, labeling system</li>
		<li>Recommended algorithms</li>
		<li>Insuring the accuracy of &quot;lift&quot; between and within categories</li>
		<li>How to create closed loops for recommendation algorithms</li>
	</ol>
	</li>
	<li>Logical regression, RankingSVM,</li>
	<li>Feature recognition (deep learning and automatic feature recognition for graphics)</li>
	<li>Natural language
	<ol>
		<li>Chinese word segmentation</li>
		<li>Theme model (text clustering)</li>
		<li>Text classification</li>
		<li>Extract keywords</li>
		<li>Semantic analysis, semantic parser, word2vec (vector to word)</li>
		<li>RNN long-term memory (TSTM) architecture</li>
	</ol>
	</li>
</ol>

                                            [language] => en
                                            [duration] => 21
                                            [status] => published
                                            [changed] => 1715084120
                                            [source_title] => Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
                                            [source_language] => zh-hans
                                            [cert_code] => 
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                                            [cc] => aitech
                                        )

                                    [spmllib] => stdClass Object
                                        (
                                            [course_code] => spmllib
                                            [hr_nid] => 141237
                                            [title] => Apache Spark MLlib
                                            [requirements] => <p>Knowledge of one of the following:</p>

<ul>
	<li>Java</li>
	<li>Scala</li>
	<li>Python</li>
	<li>SparkR.</li>
</ul>

                                            [overview] => <p>MLlib is Spark&rsquo;s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.</p>

<p>It divides into two packages:</p>

<ul>
	<li>
	<p>spark.mllib contains the original API built on top of RDDs.</p>
	</li>
	<li>
	<p>spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.</p>
	</li>
</ul>

<p>&nbsp;</p>

<p><strong>Audience</strong></p>

<p>This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark</p>

                                            [category_overview] => 
                                            [outline] => <h2>spark.mllib: data types, algorithms, and utilities</h2>

<ul>
	<li>Data types</li>
	<li>Basic statistics
	<ul>
		<li>summary statistics</li>
		<li>correlations</li>
		<li>stratified sampling</li>
		<li>hypothesis testing</li>
		<li>streaming significance testing</li>
		<li>random data generation</li>
	</ul>
	</li>
	<li>Classification and regression
	<ul>
		<li>linear models (SVMs, logistic regression, linear regression)</li>
		<li>naive Bayes</li>
		<li>decision trees</li>
		<li>ensembles of trees (Random Forests and Gradient-Boosted Trees)</li>
		<li>isotonic regression</li>
	</ul>
	</li>
	<li>Collaborative filtering
	<ul>
		<li>alternating least squares (ALS)</li>
	</ul>
	</li>
	<li>Clustering
	<ul>
		<li>k-means</li>
		<li>Gaussian mixture</li>
		<li>power iteration clustering (PIC)</li>
		<li>latent Dirichlet allocation (LDA)</li>
		<li>bisecting k-means</li>
		<li>streaming k-means</li>
	</ul>
	</li>
	<li>Dimensionality reduction
	<ul>
		<li>singular value decomposition (SVD)</li>
		<li>principal component analysis (PCA)</li>
	</ul>
	</li>
	<li>Feature extraction and transformation</li>
	<li>Frequent pattern mining
	<ul>
		<li>FP-growth</li>
		<li>association rules</li>
		<li>PrefixSpan</li>
	</ul>
	</li>
	<li>Evaluation metrics</li>
	<li>PMML model export</li>
	<li>Optimization (developer)
	<ul>
		<li>stochastic gradient descent</li>
		<li>limited-memory BFGS (L-BFGS)</li>
	</ul>
	</li>
</ul>

<h2>spark.ml: high-level APIs for ML pipelines</h2>

<ul>
	<li>Overview: estimators, transformers and pipelines</li>
	<li>Extracting, transforming and selecting features</li>
	<li>Classification and regression</li>
	<li>Clustering</li>
	<li>Advanced topics</li>
</ul>

                                            [language] => en
                                            [duration] => 35
                                            [status] => published
                                            [changed] => 1700037209
                                            [source_title] => Apache Spark MLlib
                                            [source_language] => en
                                            [cert_code] => 
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                                    [6] => apex
                                    [7] => storm
                                    [8] => nifi
                                    [9] => nifidev
                                    [10] => flink
                                    [11] => sparkpython
                                    [12] => graphcomputing
                                    [13] => aitech
                                    [14] => spmllib
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                                    [title] => Programming with Big Data in R
                                    [rating] => 4
                                    [delegate_and_company] => Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
                                    [body] => The subject matter and the pace were perfect.

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                                    [title] => Programming with Big Data in R
                                    [rating] => 5
                                    [delegate_and_company] => Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
                                    [body] => Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training to meet clients&#39; need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!

                                    [mc] => Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
                                    [is_mt] => 0
                                    [nid] => 282922
                                )

                        )

                    [7] => 4.5
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                    Programming with Big Data in R Training Course                </h1>
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                    <div id="overview-text" class="read-more-text less"> <p>Big Data is a term that refers to solutions destined for storing and processing large data sets. Developed by Google initially, these Big Data solutions have evolved and inspired other similar projects, many of which are available as open-source. R is a popular programming language in the financial industry.</p>
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            <div class="course-sub-details"><h3>Introduction to Programming Big Data with R (bpdR)</h3>

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            <p style="opacity: 0.5;font-size: 0.75rem;margin-top:-16px;">Please read our <a href="/privacy-statement" target="_blank"> Privacy Policy </a> to find out how we use your data</p>
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        <input type="text" name="sendEmailOption" value="2" hidden>
        
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    <div class="text-center">
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$("#courseBookingForm").validate({
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            error: function () {
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    }
});
</script>                </div>
            </div>

        </div><!-- modal-content -->
    </div><!-- modal-dialog -->
</div>
<script>
   $(document).ready(function(){
    $("#book-btn-plus").click(function() {
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<div class="modal fade courseModal" id="myModalGetQuote" tabindex="-1" role="dialog" aria-labelledby="myModalGetQuoteLabel">
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                            <!-- hidden block for removal -->
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                        <div class="col-12">
                            <label for="participants">Number of participants</label>
                            <input id="participants" type="text" value="1" class="form-control" name="participants">
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                        <div class="col-6" style="display:none;">
                            <!-- hidden block for removal -->
                            <label for="suitableDate">Date</label>
                            <input id="suitableDate" type="date" class="form-control" name="suitableDate">
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                    </div>
                    <!-- -----------------Course info starts here------------------------------------------------- -->
                    <!-- <p class="modal-header-form">Course Information</p> -->

                    <div class="row form-group">
                        <p>How do you want to take the course?</p>
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                            <label for="onsite">Client Premises</label>
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                        <!-- hidden block for removal -->
                        <label for="courseName">Course Name</label>
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                    <div class="col-md-6 form-group" style="display:none;">
                        <!-- hidden block for removal -->
                        <label for="courseDuration">Duration, hours</label>
                        <input id="courseDuration" type="text" class="form-control" name="courseDuration" value="21 Hours" readonly>
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                        <div class="col-md-6 form-group" style="display:none;">
                            <!-- hidden block for removal -->
                            <label for="siutableTime">Suitable Time</label>
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                    <div class="form-group" style="display:none;" >
                        <!-- hidden block for removal -->
                        <label for="preferredLanguage">Preferred language</label>
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                    <div class="form-group venueadress" style="display:none;" >
                        <!-- hidden block for removal -->
                        <label for="suggestedVenue" >Suggested Venue Address</label>
                        <input id="suggestedVenue" type="text" class="form-control" name="suggestedVenue">
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                    <!-- Course info ends here -->

                    <!-- <p class="modal-header-form">Additional Information</p> -->
                    <div class="form-group">
                        <label for="comments" >Comments</label>
                        <textarea id="comments" class="form-control" name="comments" style="height:3.88rem;"></textarea>
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                        <label for="newsletter" class="form-check-label mb-0">Inform me about discounts and promotions</label>
                        <p style='color:gray;font-size: 0.75rem;'>
                            <span>Please read our <a href="/privacy-statement" target="_blank"> Privacy Policy </a> to find out how we use your data</span>

                        </p>
                    </div>
                                            <!-- Hidden field -->
                        <input type="text" name="sendEmailOption" value="2" hidden>
                                        <!-- All hidden fields starts here  -->
                    <input type="text" name="courseCode" value="bigdatar" hidden>
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                    <input type="text" name="submitterDetails" value="3.137.160.137 | Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)" hidden>
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                    <input type="text" name="spam_required" value="" hidden>
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                    <div class="text-center">
                        <input type="submit" class="col-md-12 btn btn-success btn-lg book-now-btn enquiry-submit-btn" value="Submit">
                    </div>
                </form>
            </div>
        </div><!-- modal-content -->
    </div><!-- modal-dialog -->
</div>

<script>
$("#courseEnquiryForm").validate({
    rules: {
        fullName: "required",
        phoneNumber: {
            required: true,
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        emailAddress: {
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        fullName: "Required",
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    submitHandler: function(form, event) {
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        //Get UUID
        const id = create_UUID();
        const step = "1";
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        const token = getToken();
        //other
        const others = {
            "classroomLiveTrainer": "false",
            "classroomRemoteTrainer": "false",
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</script><!-- Modal -->
<div class="modal fade courseModal" id="myModalGetConsultancyEnquiry" tabindex="-1" role="dialog"
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    <div class="modal-dialog" role="document">
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            <div class="modal-header">
                <h3 class="modal-title" id="myModalGetConsultancyEnquiryLabel">
                    Programming with Big Data in R - Consultancy Enquiry                </h3>
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                <label for="emailAddress">Email *</label> <span class="consultancy-enquiry-email-address-error"></span>
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        <div class="row form-group">
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        <div class="row form-group">
            <div class="col-12">
                <label for="consultancySubject">Consultancy Subject *</label> <span class="consultancy-enquiry-consultancy-subject-error"></span>
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        <div class="row form-group">
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        <div class="row form-group">
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        <div class="row form-group">
            <div class="col-12">
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            <div class="col-12">
                <label for="workWith">Who will the consultant work with?</label>
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        <div class="row form-group">
            <div class="col-12">
                <label for="consultancy_urgency">Consultancy Urgency *</label>
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        </div>
        <div class="form-group" style="display:none;">
            <!-- hidden block for removal -->
            <label for="courseName">Course Name</label>
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        <div class="col-md-6 form-group" style="display:none;">
            <!-- hidden block for removal -->
            <label for="courseDuration">Duration, hours</label>
            <input id="courseDuration" type="text" class="form-control" name="courseDuration" value="21 Hours" readonly>
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        <!-- Course info ends here -->
        <!-- <p class="modal-header-form">Additional Information</p> -->
        <div class="form-group">
            <div class="col-12">
                <label for="comments" >Comments</label>
                <textarea id="comments" class="form-control" name="comments" style="height:3.88rem;"></textarea>
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            <input class="form-check-input" type="checkbox" name="newsletter" id="newsletter" value="true" checked>
            <label for="newsletter" class="form-check-label">Inform me about discounts and promotions            <p style='font-size:0.8rem; opacity:0.8;'>
                <span>Please read our <a style="opacity:1;" href="/privacy-statement" target="_blank"> Privacy Policy </a> to find out how we use your data</span>
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        <input type="text" name="courseCode" value="bigdatar" hidden>
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        <div class="text-center">
            <input type="submit" class="col-md-12 btn btn-success btn-lg book-now-btn consultancy-enquiry-submit-btn" value="Submit">
        </div>
    </form>
</div>

<script>
$("#consultancyEnquiryForm").validate({
    rules: {
        fullName: "required",
        phoneNumber: {
            required: true,
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        emailAddress: {
            email: true,
            required: true
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                        The subject matter and the pace were perfect.                     </p>
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                    <h5 class="delegate-company elipsis e3">Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada</h5>
                     
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                        Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.                    </p>
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<p>By the end of this training, participants will be able to:</p>

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                                <p>Apache Beam is an open source, unified programming model for defining and executing parallel data processing pipelines. It&#39;s power lies in its ability to run both batch and streaming pipelines, with execution being carried out by one of Beam&#39;s supported distributed processing back-ends: Apache Apex, Apache Flink, Apache Spark, and Google Cloud Dataflow. Apache Beam is useful for ETL (Extract, Transform, and Load) tasks such as moving data between different storage media and data sources, transforming data into a more desirable format, and loading data onto a new system.</p>

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                                <p>Apache Apex is a YARN-native platform that unifies stream and batch processing. It processes big data-in-motion in a way that is scalable, performant, fault-tolerant, stateful, secure, distributed, and easily operable.</p>

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                                <p>Apache Storm is a distributed, real-time computation engine used for enabling real-time business intelligence. It does so by enabling applications to reliably process unbounded streams of data (a.k.a. stream processing).</p>

<p>&quot;Storm is for real-time processing what Hadoop is for batch processing!&quot;</p>

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<p>Some of the topics included in this training include:</p>

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<p>Request this course now!</p>

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<p><strong>Format of the course</strong></p>

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<p>By the end of this training, participants will be able to:</p>

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	<li>Source, transform and manage data from disparate, distributed data sources, including databases and big data lakes.</li>
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	<li>Apply various approaches for data ingestion.</li>
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<p>By the end of this training, participants will be able to:</p>

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                                <p>This instructor-led, live training in Portugal&nbsp;(online or onsite) introduces the principles and approaches behind distributed stream and batch data processing, and walks participants through the creation of a real-time, data streaming application in Apache Flink.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Set up an environment for developing data analysis applications.</li>
	<li>Understand how Apache Flink&#39;s&nbsp;graph-processing library (Gelly) works.</li>
	<li>Package, execute, and monitor Flink-based, fault-tolerant, data streaming applications.</li>
	<li>Manage diverse workloads.</li>
	<li>Perform advanced analytics.</li>
	<li>Set up a multi-node Flink cluster.</li>
	<li>Measure and optimize performance.</li>
	<li>Integrate Flink with different Big Data systems.</li>
	<li>Compare Flink capabilities with those of other big data processing frameworks.</li>
</ul>
                            </div>
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                            21 Hours                        </span>
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                            <div role="definition" class="elipsis e4" style="margin:1rem 0.15rem; min-height:5.4rem">
                                <p>In this instructor-led, live training in Portugal, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Learn how to use Spark with Python to analyze Big Data.</li>
	<li>Work on exercises that mimic real world cases.</li>
	<li>Use different tools and techniques for big data analysis using PySpark.</li>
</ul>
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                            28 Hours                        </span>
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                            <div role="definition" class="elipsis e4" style="margin:1rem 0.15rem; min-height:5.4rem">
                                <p>In this instructor-led, live training in Portugal, participants will learn about the technology offerings and implementation approaches for processing graph data. The aim is to identify real-world objects, their characteristics and relationships, then model these relationships and process them as data using a Graph Computing (also known as Graph Analytics) approach. We start with a broad overview and narrow in on specific tools as we step through a series of case studies, hands-on exercises and live deployments.</p>

<p>By the end of this training, participants will be able to:</p>

<ul>
	<li>Understand how graph data is persisted and traversed.</li>
	<li>Select the best framework for a given task (from graph databases to batch processing frameworks.)</li>
	<li>Implement Hadoop, Spark, GraphX and Pregel to carry out graph computing across many machines in parallel.</li>
	<li>View real-world big data problems in terms of graphs, processes and traversals.</li>
</ul>
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                            <h2 class="text-black hover-text-underline elipsis">Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP</h2>
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                            21 Hours                        </span>
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                                <p>This course is aimed at developers and data scientists who wish to understand and implement AI within their applications. Special focus is given to Data Analysis, Distributed AI and NLP.</p>
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                            <h2 class="text-black hover-text-underline elipsis">Apache Spark MLlib</h2>
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                            35 Hours                        </span>
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                            <div role="definition" class="elipsis e4" style="margin:1rem 0.15rem; min-height:5.4rem">
                                <p>MLlib is Spark&rsquo;s machine learning (ML) library. Its goal is to make practical machine learning scalable and easy. It consists of common learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction, as well as lower-level optimization primitives and higher-level pipeline APIs.</p>

<p>It divides into two packages:</p>

<ul>
	<li>
	<p>spark.mllib contains the original API built on top of RDDs.</p>
	</li>
	<li>
	<p>spark.ml provides higher-level API built on top of DataFrames for constructing ML pipelines.</p>
	</li>
</ul>

<p>&nbsp;</p>

<p><strong>Audience</strong></p>

<p>This course is directed at engineers and developers seeking to utilize a built in Machine Library for Apache Spark</p>
                            </div>
                            <div class='hover-text-underline' style='color:var(--bs-link-color);'> Read more...</div>
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