Big Data Training in Prague

This instructor-led live training in Prague (online or onsite) targets intermediate-level data scientists and engineers who wish to employ 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 unifies big data, AI, and governance into a single 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 who want to effectively leverage the Rocket and Intelligence modules in Stratio with PySpark. The focus is on mastering looping structures, user-defined functions, and implementing advanced data logic.

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

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

Course Format

• Interactive lectures and discussions.
• Numerous exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Data Warehousing involves the practice of designing, constructing, and managing centralized data repositories that support analytics, reporting, and decision-making.

This instructor-led live training (online or onsite) targets intermediate-level data professionals who want to model dimensional data, build robust ETL pipelines, and optimize analytical workloads.

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

• Explain core data warehousing concepts and architectures.
• Design dimensional models and choose between star and snowflake schemas.
• Build and orchestrate ETL and ELT pipelines reliably.
• Differentiate OLTP versus OLAP workloads and optimize for analytics.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice.
• Hands-on implementation in a live lab environment.

Course Customization Options

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

Participants who complete this instructor-led, live training in Prague will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools.

Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class.

The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability.

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

This instructor-led, live training (available online or onsite) targets intermediate to advanced technical users seeking to grasp the internal architecture of Greenplum, create optimized data models, and apply high-performance SQL in distributed settings.

Upon completing this training, participants will be able to:

• Grasp Greenplum’s shared-nothing architecture and parallelism model.
• Design distributed table structures and partitions that optimize query performance.
• Apply indexing strategies, storage formats, and physical design techniques.
• Interpret EXPLAIN plans and enhance query performance using best practices.

Format of the Course

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

Course Customization Options

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

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

This instructor-led live training, available either online or onsite, targets intermediate-level administrators looking to effectively install, configure, and manage Greenplum environments, with a specific focus on handling system updates and libraries.

Upon completing this training, participants will be equipped to:

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

Course Format

• Interactive lectures and discussions.
• Practical, hands-on experience with Greenplum in a controlled lab environment.
• Guided exercises centered on cluster setup, upgrade workflows, and system extensions.

Course Customization Options

• For customized training tailored to your specific environment or data platform, please contact us to make arrangements.

This instructor-led, live training in Prague (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 Prague (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 of Iceberg tables.
• Understand the process of migrating data from other table formats to Iceberg.

This instructor-led, live training in Prague (online or onsite) is aimed at intermediate-level IT professionals who wish to enhance their skills in data architecture, governance, cloud computing, and big data technologies to effectively manage and analyze large datasets for data migration within their organizations.

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

• Understand the foundational concepts and components of various data architectures.
• Gain a comprehensive understanding of data governance principles and their importance in regulatory environments.
• Implement and manage data governance frameworks such as Dama and Togaf.
• Leverage cloud platforms for efficient data storage, processing, and management.

This instructor-led, live training in Prague (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 Prague (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 utilizing sophisticated methods and tools to examine vast, complex datasets, thereby generating actionable insights and supporting strategic decision-making.

This instructor-led live training, available online or onsite, is designed for advanced-level data professionals who aim to leverage state-of-the-art analytical methods and big data technologies for predictive, prescriptive, and real-time analytics.

Upon completing this training, participants will be able to:

• Design and implement large-scale data processing pipelines for both structured and unstructured data.
• Apply advanced machine learning and deep learning techniques to handle 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.
• Extensive 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 in Prague (online or onsite) is targeted at intermediate-level IT professionals who desire a comprehensive grasp of IBM DataStage from both administrative and development perspectives, enabling them to effectively manage and utilize this tool in their workplaces.

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

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

This instructor-led, live training in Prague (online or onsite) is designed for system administrators with beginner to intermediate skill levels who want to deploy, maintain, and optimize Spark clusters.

Upon completing this training, participants will be able to:

• Install and configure Apache Spark across 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 Prague, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

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

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

Advancements in technology and the exponential growth of data are reshaping business operations across various sectors, including the public sector. Government data generation and digital archiving rates are accelerating due to the rapid proliferation of mobile devices and applications, smart sensors, cloud computing solutions, and citizen-facing portals. As digital information expands and becomes more complex, the management, processing, storage, security, and disposition of this data also grow more intricate. New capture, search, discovery, and analysis tools are enabling organizations to extract valuable insights from unstructured data. The government sector is reaching a tipping point, recognizing information as a strategic asset that must be protected, leveraged, and analyzed—both structured and unstructured—to better serve missions and meet requirements. As government leaders strive to evolve into data-driven organizations, they are laying the groundwork to correlate dependencies across events, people, processes, and information.

High-value government solutions will emerge from a combination of 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 enables government entities to make better decisions by acting on patterns revealed through the analysis of large volumes of data—both related and unrelated, structured and unstructured.

However, achieving these goals requires far more than simply accumulating massive quantities of data. As Tom Kalil and Fen Zhao of the White House Office of Science and Technology Policy wrote on the OSTP Blog, "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."

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

The challenges posed by Big Data are nearly as daunting as its promise is encouraging. Efficient data storage is one such challenge. With budgets always tight, agencies must minimize the per-megabyte cost of storage while keeping data easily accessible so users can retrieve it when and how they need it. Backing up massive quantities of data further heightens this challenge.

Effective data analysis presents another major hurdle. Many agencies use commercial tools to sift through vast amounts of data, identifying trends that help them operate more efficiently. A recent study by MeriTalk revealed that federal IT executives believe Big Data could help agencies save over $500 billion while also fulfilling mission objectives.

Custom-developed Big Data tools are also allowing agencies to address their analytical 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 find links that can alert doctors to aortic aneurysms before they occur. It is also used for more routine tasks, such as sifting through resumes to connect job candidates with hiring managers.

During this instructor-led, live training in Prague, participants will cultivate the mindset necessary to approach Big Data technologies, assess their impact on existing processes and policies, and implement these technologies to identify criminal activity and prevent crime. We will analyze case studies from law enforcement organizations worldwide to gain insights into their adoption approaches, challenges, and results.

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

• Combine Big Data technology with traditional data gathering processes to piece together a story during an investigation.
• Implement industrial big data storage and processing solutions for data analysis.
• Prepare a proposal for the adoption of the most adequate tools and processes for enabling a data-driven approach to criminal investigation.

Big Data denotes the category of solutions designed for storing and processing extensive data sets. Initially developed by Google, these Big Data solutions have evolved and inspired numerous similar open-source projects. R is a widely used programming language within the financial sector.

Course Overview

This course offers a practical, structured introduction to constructing real-time data streaming systems. It explores core concepts, architectural patterns, and industry-standard tools for processing continuous data at scale. Participants will gain the skills to design, implement, and optimize streaming pipelines using modern frameworks. The curriculum advances from foundational principles to hands-on applications, empowering learners to confidently develop production-grade real-time solutions.

Training Format

• Instructor-led sessions with 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

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

• Distinguish between batch and streaming data processing models

• Design scalable and fault-tolerant streaming pipelines

• Utilize distributed streaming tools and frameworks

• Apply event time processing, windowing, and stateful operations

• Build and optimize real-time data solutions tailored to business needs

Apache NiFi is an open-source platform for data integration and event processing that operates on a flow-based model. It facilitates automated, real-time routing, transformation, and mediation of data between disparate systems, supported by a web-based user interface and granular control mechanisms.

This instructor-led live training, available either onsite or remotely, targets intermediate-level administrators and engineers looking to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completion of this course, participants will be equipped to:

• Install, configure, and maintain Apache NiFi clusters.
• Design and manage dataflows originating from and terminating at various sources and sinks.
• Implement logic for flow automation, routing, and transformation.
• Optimize performance, monitor system operations, and resolve issues.

Course Format

• Interactive lectures combined with discussions on real-world architectures.
• Practical labs focused on building, deploying, and managing data flows.
• Scenario-based exercises conducted in a live laboratory environment.

Course Customization Options

• For customized training arrangements, please contact us.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by leveraging distributed computing principles.

This instructor-led live training in Prague (online or onsite) is aimed at engineers who wish to set up and deploy an 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 significant effort before yielding tangible results. This course is designed to help you overcome that initial hurdle. Upon completion, participants will gain a solid understanding of Apache Spark fundamentals, clearly distinguish between RDDs and DataFrames, and become proficient with both the Python and Scala APIs. You will also develop a deep understanding of executors, tasks, and other core concepts. Adhering to industry best practices, the curriculum places strong emphasis on cloud deployment strategies, with a focus on Databricks and AWS environments. Additionally, students will explore the distinctions between AWS EMR and AWS Glue, examining one of the latest Spark-based services offered by AWS.

AUDIENCE:

Data Engineers, DevOps Engineers, Data Scientists

This instructor-led live training in Prague (online or on-site) is aimed at technical professionals who wish to deploy Talend Open Studio for Big Data to simplify the process of reading and analyzing 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 Big Data components and connectors within Open Studio.
• 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.