PySpark Training in Prague

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.

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.