PySpark Training in Nigeria

This instructor-led live training in Nigeria 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 instructor-led, live training held in Nigeria, participants will explore the integration of Python and Spark for big data analysis, engaging in practical, hands-on exercises.

By the end of this training, participants will be capable of:

• Using Spark with Python to analyze Big Data.
• Completing exercises that mirror real-world applications.
• Employing diverse tools and techniques for big data analysis via PySpark.

This instructor-led, live training (online or onsite) is targeted at data engineers, analysts, and data professionals who intend to utilise Databricks and PySpark for constructing 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.

Spanning three days, this hands-on course is designed to help participants construct and refine high-efficiency data-processing workloads leveraging PySpark, Pandas, and Polars within Kubernetes-based infrastructures.

Learners will gain a practical grasp of how Spark applications execute on Kubernetes, understanding how specific application-level configuration choices impact performance, scalability, resource usage, and overall costs. The curriculum addresses critical optimization areas such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behavior, the small-file problem, and efficient Parquet processing.

Additionally, the course tackles common hurdles encountered 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 practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate different configuration strategies, and apply optimization techniques to realistic ETL and machine learning scenarios.

The core focus of the course remains on practical decision-making: identifying bottlenecks, selecting the right tools, configuring Spark effectively, and balancing performance against infrastructure resource consumption and costs.