PySpark Training in Portugal

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

In this instructor-led, live training in Portugal, participants will gain proficiency in using Python and Spark together for big data analysis through hands-on exercises.

Upon completion of this training, participants will be able to:

• Apply Spark and Python for in-depth Big Data analysis.
• Complete exercises that reflect real-world use cases.
• Utilise diverse tools and techniques for big data analysis with PySpark.

This instructor-led live training (online or onsite) targets data engineers, data analysts, and data professionals aiming to build scalable data pipelines using Databricks and PySpark, as well as those seeking to migrate existing SQL workflows.

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

This intensive three-day workshop is dedicated to constructing and refining high-efficiency data-processing workloads that leverage PySpark, Pandas, and Polars within Kubernetes-based environments.

Learners will gain a practical insight into the execution mechanics of Spark applications on Kubernetes, exploring how application-level configuration choices directly impact performance, scalability, resource consumption, and overall cost. Key optimisation topics include executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the small-file problem, and the efficient handling of Parquet data.

The curriculum also 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 interactive exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

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