PySpark Training in Norway

In this instructor-led, live training held in Norway, participants will learn to integrate Python and Spark for big data analysis through hands-on exercises.

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

• Utilize Spark with Python to analyze Big Data.
• Complete exercises that mimic real-world cases.
• Employ different tools and techniques for big data analysis using PySpark.

This instructor-led live training (available 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 constructing scalable data processing and Machine Learning workflows using 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 course is dedicated to constructing and fine-tuning high-efficiency data processing workloads within Kubernetes-based environments, leveraging the combined power of PySpark, Pandas, and Polars.

Learners will cultivate a hands-on grasp of how Spark applications function on Kubernetes, exploring how specific configuration choices directly impact performance, scalability, resource utilization, and operational costs. The curriculum delves into critical optimization domains such as executor sizing, memory allocation, dynamic resource allocation, partitioning strategies, shuffle mechanics, the small-file challenge, and the efficient handling of Parquet data.

Additionally, the course tackles typical hurdles encountered when using Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a robust, high-performance alternative for specific processing tasks. Through practical, hands-on exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

The core focus of the course remains on practical decision-making: mastering the ability to pinpoint bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource usage and cost.