Greenplum Architecture and Data Modeling Training Course

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

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

• Develop a comprehensive 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 across various 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 Slovakia (online or onsite) is intended for intermediate-level data scientists and engineers who aim to utilize Google Colab and Apache Spark for big data processing and analytics.

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

• Configure 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, data analysts, and data 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 discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This three-day practical course is centered on developing and optimizing high-efficiency data-processing workloads utilizing PySpark, Pandas, and Polars within Kubernetes-based environments.

Attendees will gain a practical comprehension of how Spark applications are executed on Kubernetes, and how configuration decisions made at the application level impact performance, scalability, resource consumption, and cost. The curriculum addresses key optimization areas, including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behavior, small-file issues, and efficient Parquet processing.

Furthermore, the course tackles common challenges encountered when using Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-performance alternative for specific data-processing tasks. Through hands-on exercises, participants will diagnose performance and memory issues, compare various configuration strategies, and apply optimization techniques to realistic ETL and machine learning scenarios.

The course places a strong emphasis on practical decision-making: enabling learners to identify bottlenecks, select the appropriate tools, configure Spark efficiently, and balance performance against infrastructure resource consumption and cost.

This instructor-led, live training in Slovakia (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 live, instructor-led training in Slovakia (conducted online or on-site) targets system administrators at beginner to intermediate levels who intend to deploy, manage, and optimize Spark clusters.

By the conclusion of this program, learners will be capable of:

• Installing and setting up Apache Spark in various environments.
• Managing cluster resources and monitoring Spark applications.
• Improving the performance of Spark clusters.
• Enforcing security measures and ensuring high availability.
• Troubleshooting and resolving standard Spark issues.

In this instructor-led live training held in Slovakia, participants will master the use of Python and Spark for big data analysis through immersive, hands-on exercises.

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

• Apply Spark in conjunction with Python to analyze large-scale data sets.
• Complete exercises that reflect practical, real-world applications.
• Leverage diverse tools and methodologies for big data analysis using PySpark.

This instructor-led, live training in Slovakia (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 live, instructor-led session in Slovakia explores the Stratio platform, highlighting the Rocket and Intelligence modules through PySpark. Attendees will develop expertise in data ingestion, transformation, and advanced analytics, while acquiring practical proficiency in loops, UDFs, and enterprise-grade data workflows.