PySpark Training in Denmark

This live, instructor-led course in Denmark dives deep into the Stratio platform, specifically highlighting the integration of Rocket and Intelligence modules with PySpark. Learners will gain mastery over data ingestion, transformation, and advanced analytics, while building practical skills in loops, UDFs, and enterprise-grade data workflows.

In this live, instructor-led training held in Denmark, participants will explore how to combine Python and Spark for big data analysis, supported by hands-on practical exercises.

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

• Apply Spark and Python to analyze big data effectively.
• Engage in practical exercises that simulate real-world conditions.
• Employ various tools and techniques for big data analysis using PySpark.

This live, instructor-led training (conducted online or on-site) is designed for data engineers, data analysts, and data professionals seeking to utilize Databricks and PySpark to construct scalable data pipelines and transition existing SQL workflows.

This training delivers a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants learn how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

This intensive three-day workshop is designed to equip professionals with the skills to construct and refine high-performance data-processing workloads leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Attendees will gain a functional understanding of how Spark applications operate on Kubernetes, focusing on how configuration choices at the application level directly impact performance, scalability, resource utilisation, and operational costs. The curriculum addresses critical optimisation domains, such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the impact of small-file issues, and efficient Parquet handling.

Additionally, the course tackles frequent challenges 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 approaches, and implement optimisation strategies for realistic ETL and machine learning use cases.

The core focus of the course remains on practical decision-making: mastering the ability to pinpoint bottlenecks, select the most appropriate tooling, configure Spark effectively, and achieve a balance between performance and infrastructure resource consumption and cost.