PySpark Training in Lille

This instructor-led live training in Lille delves into the Stratio platform, with a specific focus on the Rocket and Intelligence modules using PySpark. Participants will gain mastery over data ingestion, transformation, and advanced analytics, while developing practical skills in loops, UDFs, and enterprise data workflows.

This instructor-led, live training in Lille guides participants in harnessing the power of Python and Spark to analyze big data, supported by engaging hands-on exercises.

By the end of the session, participants will be able to:

• Utilize Spark and Python together for effective big data analysis.
• Complete exercises that simulate real-world professional challenges.
• Employ various tools and techniques for big data analytics via PySpark.

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

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and how to effectively manage large datasets by applying distributed computing principles.

This three-day practical course focuses on building and optimising efficient data-processing workloads using PySpark, Pandas and Polars in Kubernetes-based environments.

Participants will develop a practical understanding of how Spark applications execute on Kubernetes and how application-level configuration decisions influence performance, scalability, resource consumption and cost. The course covers key optimisation areas including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behaviour, small-file problems and efficient Parquet processing.

The course also addresses common challenges when working with Pandas, including memory limitations and out-of-memory failures, and introduces Polars as a high-performance alternative for selected data-processing workloads. Through hands-on exercises, participants will diagnose performance and memory issues, compare different configuration strategies and apply optimisation techniques to realistic ETL and machine learning scenarios.

The emphasis throughout the course is on practical decision-making: understanding how to identify bottlenecks, select the appropriate tool, configure Spark efficiently and balance performance with infrastructure resource consumption and cost.