Big Data Training in Évora

This instructor-led, live training in Évora (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.

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.

Data Warehousing involves the design, construction, and operation of centralized data repositories that facilitate analytics, reporting, and informed decision-making.

This instructor-led live training, available online or onsite, targets intermediate-level data professionals aiming to model dimensional data, construct robust ETL pipelines, and optimize analytical workloads.

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

• Articulate fundamental data warehousing concepts and architectures.
• Design dimensional models and select appropriate star or snowflake schemas.
• Construct and orchestrate reliable ETL and ELT pipelines.
• Distinguish between OLTP and OLAP workloads and optimize them for analytical tasks.

Course Format

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

Customization Options

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

By completing this instructor-led, live training in Évora, participants will acquire a practical, real-world grasp of Big Data, along with its associated technologies, methodologies, and tools.

Attendees will be able to apply this knowledge through hands-on exercises. The class places significant emphasis on group interaction and feedback from the instructor.

The course begins with an overview of fundamental Big Data concepts, then moves on to the programming languages and methodologies employed for Data Analysis. Finally, we examine the tools and infrastructure that facilitate Big Data storage, Distributed Processing, and Scalability.

Greenplum is an open-source massively parallel processing (MPP) data platform engineered for analytics and large-scale data environments.

This instructor-led live training, available online or onsite, targets intermediate to advanced technical users seeking to master Greenplum's internal architecture, construct optimized data models, and implement high-performance SQL in distributed settings.

Upon completion, participants will be able to:

• Comprehend Greenplum’s shared-nothing architecture and parallelism model.
• Design distributed table structures and partitions tailored for query performance.
• Apply indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and refine queries according to best practices.

Course Format

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

Customization Options

• To request tailored training for this course, please contact us to make arrangements.

Greenplum is an open-source massively parallel data platform built on PostgreSQL, designed for advanced analytics and large-scale data warehousing.

This instructor-led live training (available online or onsite) is designed for intermediate-level administrators who want to effectively install, configure, and manage Greenplum environments, including handling system updates and libraries.

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

• Install and configure a production-ready Greenplum cluster.
• Manage system updates and safely apply patches.
• Handle Greenplum library management for analytics and extensions.
• Troubleshoot and monitor Greenplum systems.

Format of the Course

• Interactive lecture and discussion.
• Hands-on use of Greenplum in a controlled lab environment.
• Guided exercises focused on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• To request a customized training for this course based on your environment or data platform, please contact us to arrange.

This instructor-led live training in Évora (online or onsite) is aimed at advanced-level data professionals who wish to optimize data processing workflows, ensure data integrity, and implement robust data lakehouse solutions that can handle the complexities of modern big data applications.

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

• Gain an in-depth understanding of Iceberg’s architecture, including metadata management and file layout.
• Configure Iceberg for optimal performance in various environments and integrate it with multiple data processing engines.
• Manage large-scale Iceberg tables, perform complex schema changes, and handle partition evolution.
• Master techniques to optimize query performance and data scan efficiency for large datasets.
• Implement mechanisms to ensure data consistency, manage transactional guarantees, and handle failures in distributed environments.

This instructor-led, live training in Évora (online or onsite) is aimed at beginner-level data professionals who wish to acquire the knowledge and skills necessary to effectively utilize Apache Iceberg for managing large-scale datasets, ensuring data integrity, and optimizing data processing workflows.

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

• Gain a thorough understanding of Apache Iceberg's architecture, features, and benefits.
• Learn about table formats, partitioning, schema evolution, and time travel capabilities.
• Install and configure Apache Iceberg in different environments.
• Create, manage, and manipulate Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Évora (available online or on-site) targets intermediate IT professionals aiming to boost their competencies in data architecture, governance, cloud computing, and big data technologies. The program focuses on effectively managing and analyzing large datasets for organizational data migration.

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

• Comprehend the fundamental concepts and components of diverse data architectures.
• Develop a thorough understanding of data governance principles and their critical role in regulatory contexts.
• Deploy and oversee data governance frameworks, including DAMA and TOGAF.
• Utilize cloud platforms to facilitate efficient data storage, processing, and management.

This instructor-led, live training in Évora (online or onsite) is aimed at intermediate-level data engineers who wish to learn how to use Azure Data Lake Storage Gen2 for effective data analytics solutions.

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

• Understand the architecture and key features of Azure Data Lake Storage Gen2.
• Optimize data storage and access for cost and performance.
• Integrate Azure Data Lake Storage Gen2 with other Azure services for analytics and data processing.
• Develop solutions using the Azure Data Lake Storage Gen2 API.
• Troubleshoot common issues and optimize storage strategies.

This instructor-led, live training in Évora (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.

Big Data and Advanced Analytics involves the application of sophisticated techniques and tools to analyse large, complex datasets to derive actionable insights and support strategic decision-making.

This instructor-led, live training (available online or onsite) is designed for advanced-level data professionals who wish to leverage cutting-edge analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

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

• Design and implement large-scale data processing pipelines for structured and unstructured data.
• Apply advanced machine learning and deep learning techniques to massive datasets.
• Leverage distributed computing frameworks for real-time analytics and data streaming.
• Integrate big data analytics into business intelligence and decision-making systems.

Format of the Course

• Interactive lecture and discussion.
• Ample exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

This instructor-led live training, conducted in Évora (either online or onsite), is targeted at intermediate-level IT professionals who aspire to gain a comprehensive understanding of IBM DataStage from both administrative and developmental perspectives. This enables them to manage and utilize the tool effectively within their respective workplaces.

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

• Comprehend the core concepts of DataStage.
• Learn how to install, configure, and manage DataStage environments effectively.
• Connect to various data sources and efficiently extract data from databases, flat files, and external sources.
• Implement effective data loading techniques.

This instructor-led, live training in Évora (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.

In this instructor-led, live training in Évora, 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.

Technological advancements and the exponential growth of information are fundamentally reshaping business operations across various sectors, including the government sector. The volume of data generated and digitized by governments is increasing rapidly, driven by the proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing digital portals. As digital information expands and grows more complex, the management, processing, storage, security, and disposal of that information also become more challenging. Emerging tools for capture, search, discovery, and analysis are enabling organizations to derive valuable insights from unstructured data. The government sector is reaching a critical juncture, recognizing information as a strategic asset. To better serve citizens and fulfill mission requirements, governments must protect, leverage, and analyze both structured and unstructured data. As government leaders work to evolve their organizations into data-driven entities, they are establishing the foundation to correlate dependencies across events, personnel, processes, and information.

High-impact government solutions are being developed by integrating the most disruptive technologies:

• Mobile devices and applications
• Cloud services
• Social business technologies and networking
• Big Data and analytics

Big Data represents an intelligent industry solution that empowers governments to make superior decisions by acting on patterns identified through the analysis of vast volumes of data—whether related or unrelated, structured or unstructured.

However, achieving these outcomes requires more than just accumulating large amounts of data. "Making sense of these volumes of Big Data requires cutting-edge tools and technologies that can analyze and extract useful knowledge from vast and diverse streams of information," Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote in a post on the OSTP Blog.

The White House took a significant step toward helping agencies identify these technologies by establishing the National Big Data Research and Development Initiative in 2012. This initiative allocated over $200 million to maximize the potential of the Big Data explosion and the tools required to analyze it.

The challenges posed by Big Data are as formidable as its potential benefits are encouraging. Efficient data storage is one key challenge. With budgets remaining tight, agencies must minimize the cost per megabyte of storage while ensuring data remains easily accessible so users can retrieve it whenever and however they need it. Backing up these massive data volumes further intensifies the challenge.

Effective data analysis is another major hurdle. Many agencies utilize commercial tools that allow them to sift through vast amounts of data, identifying trends that enhance operational efficiency. (A recent study by MeriTalk found that federal IT executives believe Big Data could help agencies save over $500 billion while also meeting mission objectives.)

Custom-developed Big Data tools are also enabling agencies to meet their data analysis needs. For instance, the Oak Ridge National Laboratory’s Computational Data Analytics Group has made its Piranha data analytics system available to other agencies. This system has helped medical researchers identify links that can alert doctors to aortic aneurysms before they occur. It is also used for routine tasks, such as sifting through resumes to match job candidates with hiring managers.

In this instructor-led live training in Évora, participants will learn the mindset required to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify criminal activity and prevent crime. Case studies from law enforcement organizations worldwide will be examined to gain insights into their adoption approaches, challenges, and results.

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

• Integrate Big Data technology with traditional data gathering processes to construct a narrative during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for adopting the most suitable tools and processes to enable a data-driven approach to criminal investigation.

Big Data refers to the range of solutions designed for storing and processing extensive data sets. Originally developed by Google, these Big Data solutions have advanced and inspired numerous similar initiatives, many of which are now available as open-source projects. R has become a widely adopted programming language within the financial sector.

Course Overview

This course offers a practical and structured introduction to constructing real-time data streaming systems. It addresses core concepts, architectural patterns, and industry-standard tools essential for processing continuous data at scale. Participants will acquire the skills to design, implement, and optimise streaming pipelines using contemporary frameworks. The curriculum advances from foundational theories to practical applications, empowering learners to confidently develop production-ready real-time solutions.

Training Format

• Instructor-led sessions featuring guided explanations

• Concept walkthroughs supported by real-world examples

• Hands-on demonstrations and coding exercises

• Progressive labs aligned with daily topics

• Interactive discussions and Q&A sessions

Course Objectives

• Understand the concepts and system architecture of real-time data streaming

• Differentiate between batch and streaming data processing models

• Design scalable and fault-tolerant streaming pipelines

• Utilise distributed streaming tools and frameworks

• Apply event time processing, windowing, and stateful operations

• Build and optimise real-time data solutions for specific business use cases

Apache NiFi is an open-source, flow-based data integration and event-processing platform. It enables automated, real-time data routing, transformation, and system mediation between disparate systems, with a web-based UI and fine-grained control.

This instructor-led, live training (onsite or remote) is aimed at intermediate-level administrators and engineers who wish to deploy, manage, secure, and optimize NiFi dataflows in production environments.

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

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows from varied sources and sinks.
• Implement flow automation, routing, and transformation logic.
• Optimize performance, monitor operations, and troubleshoot issues.

Format of the Course

• Interactive lecture with real-world architecture discussion.
• Hands-on labs: building, deploying, and managing flows.
• Scenario-based exercises in a live-lab environment.

Course Customization Options

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

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 Évora (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.

The initial learning curve for Apache Spark can be steep, requiring considerable effort to achieve early results. This course is designed to help you navigate that challenging first phase. Upon completion, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and become proficient with the Python and Scala APIs. You will also gain a solid understanding of executors, tasks, and other core concepts. Aligning with best practices, the course places strong emphasis on cloud deployment, specifically within Databricks and AWS environments. Additionally, students will learn to differentiate between AWS EMR and AWS Glue, one of AWS's most recent Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

This instructor-led, live training in Évora (online or onsite) is designed for technical professionals looking to deploy Talend Open Studio for Big Data to streamline the process of reading and analysing big data.

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

• Install and configure Talend Open Studio for Big Data.
• Connect with big data systems such as Cloudera, HortonWorks, MapR, Amazon EMR, and Apache.
• Understand and set up Open Studio's big data components and connectors.
• Configure parameters to automatically generate MapReduce code.
• Use Open Studio's drag-and-drop interface to run Hadoop jobs.
• Prototype big data pipelines.
• Automate big data integration projects.