PySpark Training in Romania

This live, instructor-led training in Romania explores the Stratio platform, with a specific focus on the Rocket and Intelligence modules integrated with PySpark. Participants will gain mastery in data ingestion, transformation, and advanced analytics, developing practical expertise in loops, UDFs, and enterprise data workflows.

In this live, instructor-led training in Romania, participants will develop the skills to combine Python and Spark for big data analysis through practical, hands-on exercises.

Upon completion, participants will be equipped to:

• Utilize Spark alongside Python to effectively analyze Big Data.
• Engage in exercises that simulate real-world data scenarios.
• Apply diverse tools and techniques for big data analysis leveraging PySpark.

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

This course offers a hands-on introduction to developing scalable data processing and Machine Learning workflows using PySpark. Participants will discover 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 workshop is designed to help you build and fine-tune high-efficiency data processing workflows utilizing PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Attendees will gain a deep, hands-on grasp of how Spark applications function on Kubernetes. You will learn how configuration choices at the application level directly impact performance, scalability, resource utilization, and operational costs. The curriculum delves into critical optimization strategies, such as determining optimal executor sizing, managing memory allocation, leveraging dynamic allocation, refining partitioning tactics, understanding shuffle dynamics, resolving the small-file challenge, and achieving efficient Parquet processing.

The training also tackles frequent hurdles encountered when using Pandas, specifically memory constraints and out-of-memory errors. It introduces Polars as a high-performance alternative for specific data processing tasks. Through practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and apply optimization techniques to real-world ETL and machine learning use cases.

Throughout the course, the primary focus remains on practical decision-making: mastering the ability to pinpoint bottlenecks, choose the right tool, configure Spark for maximum efficiency, and strike a balance between high performance and the consumption of infrastructure resources and costs.