Apache Spark in the Cloud Training Course

This instructor-led, live training in Portugal (online or onsite) is aimed at intermediate-level data scientists and engineers who wish to use Google Colab and Apache Spark for big data processing and analytics.

By the end of this training, participants will be able to:

• Set up a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Big data analytics entails the examination of vast and diverse datasets to identify correlations, uncover hidden patterns, and extract valuable insights.

The healthcare sector generates enormous volumes of complex, heterogeneous medical and clinical data. Leveraging big data analytics within this domain holds significant potential for generating insights that enhance healthcare delivery. Nevertheless, the sheer scale of these datasets presents substantial challenges for analysis and practical implementation in clinical settings.

In this instructor-led, live remote training, participants will learn how to conduct big data analytics in health through a series of hands-on live laboratory exercises.

By the end of this training, participants will be able to:

• Install and configure big data analytics tools such as Hadoop MapReduce and Spark
• Understand the key characteristics of medical data
• Apply big data techniques to manage medical data
• Study big data systems and algorithms within the context of health applications

Audience

• Developers
• Data Scientists

Format of the Course

• A blend of lectures, discussions, exercises, and intensive hands-on practice.

Note

• To request a customized training for this course, please contact us to arrange.

This instructor-led live training in Portugal (online or onsite) is aimed at system administrators who wish to learn how to set up, deploy and manage Hadoop clusters within their organisation.

By the end of this training, participants will be able to:

• Install and configure Apache Hadoop.
• Understand the four major components in the Hadoop ecoystem: HDFS, MapReduce, YARN, and Hadoop Common.
• Use Hadoop Distributed File System (HDFS) to scale a cluster to hundreds or thousands of nodes.  
• Set up HDFS to operate as storage engine for on-premise Spark deployments.
• Set up Spark to access alternative storage solutions such as Amazon S3 and NoSQL database systems such as Redis, Elasticsearch, Couchbase, Aerospike, etc.
• Carry out administrative tasks such as provisioning, management, monitoring and securing an Apache Hadoop cluster.

In this instructor-led, live training in Portugal (onsite or remote), participants will learn how to set up and integrate different Stream Processing frameworks with existing big data storage systems and related software applications and microservices.

By the end of this training, participants will be able to:

• Install and configure different Stream Processing frameworks, such as Spark Streaming and Kafka Streaming.
• Understand and select the most appropriate framework for the job.
• Process of data continuously, concurrently, and in a record-by-record fashion.
• Integrate Stream Processing solutions with existing databases, data warehouses, data lakes, etc.
• Integrate the most appropriate stream processing library with enterprise applications and microservices.

This course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently handle large datasets by applying distributed computing principles.

This instructor-led, live training in Portugal (online or onsite) is designed for data scientists who wish to employ the SMACK stack to construct data processing platforms for big data solutions.

By the conclusion of this training, participants will be able to:

• Implement a data pipeline architecture for processing big data.
• Develop cluster infrastructure with Apache Mesos and Docker.
• Analyze data with Spark and Scala.
• Manage unstructured data with Apache Cassandra.

This instructor-led, live training in Portugal (online or onsite) is aimed at engineers who wish to set up and deploy Apache Spark system for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led, live training in Portugal (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

OBJECTIVE:

This course introduces Apache Spark, teaching students how Spark integrates into the Big Data ecosystem and how to leverage it for data analysis. Topics include the Spark shell for interactive analysis, internal mechanics, APIs, Spark SQL, Spark Streaming, Machine Learning, and GraphX.

AUDIENCE:

Developers and Data Analysts

This instructor-led, live training in Portugal (online or onsite) is designed for data scientists and developers who wish to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in Portugal, participants will learn how to leverage Python and Spark together to analyze big data while engaging in hands-on exercises.

By the end of this training, participants will be able to:

• Master the use of Spark with Python for Big Data analysis.
• Complete exercises that simulate real-world scenarios.
• Utilize various tools and techniques for big data analysis using PySpark.

This instructor-led, live training in Portugal (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Spark SQL is the module within Apache Spark designed for handling structured and unstructured data. It provides metadata regarding the data structure and the computations being executed, enabling the system to apply optimizations. Spark SQL is commonly used for:
- executing SQL queries.
- accessing data from an existing Hive installation.

During this instructor-led live training (available onsite or remotely), participants will learn how to analyze various data sets using Spark SQL.

Upon completion of this training, participants will be able to:

• Install and configure Spark SQL.
• Conduct data analysis using Spark SQL.
• Query data sets in various formats.
• Visualize data and query outcomes.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• To arrange customized training for this course, please contact us.

Stratio is a data-centric platform that seamlessly integrates big data, AI, and governance into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics within enterprise environments.

This instructor-led live training (available online or onsite) is designed for intermediate-level data professionals aiming to effectively utilize the Rocket and Intelligence modules in Stratio with PySpark. The curriculum focuses on looping structures, user-defined functions, and advanced data logic.

Upon completing this training, participants will be able to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to ensure reusable data operations in PySpark.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical sessions.
• Hands-on implementation in a live laboratory environment.

Course Customization Options

• To arrange customized training for this course, please contact us.