PySpark Training in South Africa

This instructor-led live training in South Africa focuses on the Stratio platform, highlighting the Rocket and Intelligence modules integrated with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, while developing practical skills in loops, UDFs, and enterprise-level data workflows.

In this live, instructor-led training in South Africa, participants will learn how to combine Python and Spark to analyze big data, supported by practical hands-on exercises.

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

• Analyze big data effectively by utilizing Spark alongside Python.
• Apply their skills through exercises that reflect real-world scenarios.
• Leverage various 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 developing scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by applying distributed computing principles.

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

Participants will cultivate a deep practical understanding of Spark application execution on Kubernetes, exploring how configuration decisions at the application level directly impact performance, scalability, resource usage, and operational costs. The curriculum addresses critical optimization domains such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the small-file challenge, and the efficient handling of Parquet files.

The course also navigates common pitfalls 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 hands-on labs, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimization techniques in realistic ETL and machine learning contexts.

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