PySpark Training in Australia

This live, instructor-led training in Australia focuses on the Stratio platform, specifically integrating the Rocket and Intelligence modules with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, acquiring practical skills in loops, UDFs, and enterprise-level data workflows.

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

Upon completing this training, participants will be equipped to:

• Utilize Spark alongside Python for comprehensive Big Data analysis.
• Engage with exercises that reflect real-world scenarios.
• Apply various tools and techniques for big data analysis via 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 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 program is dedicated to the construction and optimisation of high-efficiency data processing workloads leveraging PySpark, Pandas and Polars within Kubernetes environments.

Attendees will cultivate a hands-on understanding of Spark application execution on Kubernetes, specifically examining how application-level configuration choices 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 small-file problem, and efficient Parquet processing.

Furthermore, the course tackles prevalent challenges associated with Pandas, including 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 implement optimisation techniques in realistic ETL and machine learning contexts.

Throughout the session, the primary focus remains on practical decision-making: identifying bottlenecks, selecting the most suitable tools, configuring Spark for maximum efficiency, and striking a balance between processing performance and infrastructure resource consumption and costs.