Greenplum Architecture and Data Modeling Training Course

This instructor-led, live training in Czech Republic (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 Czech Republic (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 Czech Republic (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.

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 Czech Republic (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, conducted in Czech Republic (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 Czech Republic, 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.

This instructor-led, live training in Czech Republic (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.