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.

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.

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.

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, conducted in Prague (either online or onsite), targets system administrators at the beginner to intermediate level who intend to deploy, maintain, and optimize Spark clusters.

Once completed, participants will be capable of:

• Setting up and configuring Apache Spark in diverse environments.
• Managing cluster resources and monitoring Spark applications.
• Improving the performance of Spark clusters.
• Applying security measures and ensuring high availability.
• Debugging and fixing common Spark-related problems.

In this instructor-led, live training held in Prague, participants will explore how to combine Python and Spark for big data analysis through a series of practical, hands-on exercises.

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

• Analyze Big Data by integrating Spark with Python.
• Solve exercises modeled after real-world use cases.
• Leverage diverse tools and techniques for big data analysis using PySpark.

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.

This instructor-led live training (online or onsite) is designed for data engineers, analysts, and professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

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 intensive three-day practical course is dedicated to designing and tuning high-performance data-processing workloads within Kubernetes environments, utilising PySpark, Pandas and Polars.

Learners will gain a working knowledge of how Spark applications are executed on Kubernetes and how application-level configuration choices directly impact performance, scalability, resource utilisation and overall cost. Key optimisation topics covered include executor sizing, memory management, dynamic allocation, partitioning methods, shuffle mechanics, the challenge of small files and the efficient handling of Parquet data.

The curriculum also tackles common pain points associated with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-speed alternative for specific data-processing tasks. Through practical, hands-on exercises, participants will learn to diagnose performance and memory issues, evaluate various configuration strategies and implement optimisation techniques in realistic ETL and machine learning contexts.

The course prioritises practical decision-making, focusing on how to pinpoint bottlenecks, choose the right tools, configure Spark effectively and strike a balance between high performance and responsible infrastructure resource usage and cost management.

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.

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.