PySpark Training in Sweden

In this live, instructor-led session held in Sweden, participants will gain insights into utilizing Python and Spark for big data analysis while engaging in practical, hands-on exercises.

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

• Utilize Spark alongside Python for Big Data analysis.
• Complete exercises that simulate real-world challenges.
• Employ diverse tools and techniques for big data analysis using PySpark.

This instructor-led, live training (online or onsite) is aimed at 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 course offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows with PySpark. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to process substantial datasets efficiently by leveraging distributed computing principles.

This hands-on, three-day programme is dedicated to constructing and fine-tuning high-performance data-processing workloads by leveraging PySpark, Pandas and Polars within Kubernetes-based ecosystems.

Attendees will cultivate a practical grasp of how Spark applications are executed on Kubernetes, gaining insight into how application-level configuration choices directly impact performance, scalability, resource utilisation and overall costs. Key optimisation topics covered include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the challenges of small files, and efficient Parquet processing.

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-performance alternative for specific data-processing tasks. Through practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and apply optimisation techniques to realistic ETL and machine learning scenarios.

The core focus of the course is on practical decision-making: mastering the ability to identify performance bottlenecks, select the most appropriate tools, configure Spark efficiently, and strike a balance between performance gains and infrastructure resource consumption and cost.