PySpark Training in Kenya

In this live, instructor-led training held in Kenya, participants will learn to leverage Python and Spark together for big data analysis, supported by practical, hands-on exercises.

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

• Utilize Spark in conjunction with Python for comprehensive Big Data analysis.
• Engage with exercises that replicate real-world business scenarios.
• Apply diverse tools and methodologies for big data analysis using PySpark.

This instructor-led live training (offered online or onsite) is tailored for data engineers, analysts, and professionals aiming to build scalable data pipelines using Databricks and PySpark while migrating existing SQL workflows.

This training offers a hands-on introduction to constructing scalable data processing and Machine Learning workflows using PySpark. Participants will learn how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently process extensive datasets by applying distributed computing principles.

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

Attendees will cultivate a deep, practical grasp of how Spark applications operate on Kubernetes, exploring how strategic configuration choices directly impact performance, scalability, resource efficiency, and overall operational costs. The curriculum delves into critical optimization domains, such as executor sizing, memory distribution, dynamic allocation, partitioning methodologies, shuffle dynamics, mitigating small-file inefficiencies, and enhancing Parquet handling.

Additionally, the course tackles prevalent challenges associated with Pandas, particularly memory constraints and out-of-memory exceptions, while introducing Polars as a robust, high-performance alternative for specific data tasks. Through immersive, hands-on labs, participants will learn to troubleshoot performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

The core focus remains on practical decision-making: mastering the ability to pinpoint bottlenecks, select the right tool for the job, configure Spark effectively, and strike a balance between performance and infrastructure resource usage and cost.