PySpark Training in Botswana

This instructor-led live training in Botswana covers the Stratio platform, focusing on the Rocket and Intelligence modules with PySpark. Participants will master data ingestion, transformation, and advanced analytics, gaining practical skills in loops, UDFs, and enterprise data workflows.

In this live, instructor-led training held in Botswana, participants will discover how to combine Python and Spark for big data analysis while engaging in hands-on exercises.

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

• Utilize Spark alongside Python for Big Data analysis.
• Complete exercises that simulate real-world situations.
• Employ a variety of tools and techniques for big data analysis using PySpark.

This instructor-led live training (online or onsite) targets data engineers, analysts, and professionals seeking to utilize Databricks and PySpark for building scalable data pipelines and migrating 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 applying distributed computing principles.

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

Attendees will gain a working knowledge of how Spark applications operate on Kubernetes, along with the impact that application-level configuration choices have on overall performance, scalability, resource utilisation and operational costs. The curriculum explores critical optimisation domains such as executor sizing, memory distribution, dynamic allocation, partitioning methods, shuffle operations, mitigating small-file issues and implementing efficient Parquet handling.

Additionally, the session tackles frequent difficulties associated with Pandas, such as memory constraints and out-of-memory errors, while presenting Polars as a high-performance alternative for specific processing tasks. Through practical exercises, participants will learn to diagnose performance and memory challenges, evaluate various configuration strategies and implement optimisation techniques for realistic ETL and machine learning contexts.

Throughout the workshop, the focus remains on practical decision-making: identifying bottlenecks, choosing the right tools, configuring Spark effectively and balancing performance against infrastructure resource usage and expenditure.