Python and Spark for Big Data for Banking (PySpark) Training Course

This instructor-led, live training program, available in online or onsite formats, is designed for data engineers, analysts, and technical professionals seeking to utilize Databricks and PySpark for the construction of scalable data pipelines and the migration of existing SQL workflows to support public sector initiatives.

This training provides a practical foundation for establishing scalable data processing and Machine Learning workflows utilizing PySpark. Participants will examine the function of Apache Spark within contemporary Big Data ecosystems and acquire methods for efficiently processing extensive datasets through distributed computing principles.

This three-day practical course focuses on building and optimizing efficient data-processing workloads for government using PySpark, Pandas, and Polars in Kubernetes-based environments.

Participants will develop a practical understanding of how Spark applications execute on Kubernetes and how application-level configuration decisions influence performance, scalability, resource consumption, and cost. The course covers key optimization areas including executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle behavior, small-file problems, and efficient Parquet processing.

The course also addresses common challenges when working with Pandas, including memory limitations and out-of-memory failures, and introduces Polars as a high-performance alternative for selected data-processing workloads. Through hands-on exercises, participants will diagnose performance and memory issues, compare different configuration strategies, and apply optimization techniques to realistic ETL and machine learning scenarios.

The emphasis throughout the course is on practical decision-making: understanding how to identify bottlenecks, select the appropriate tool, configure Spark efficiently, and balance performance with infrastructure resource consumption and cost.

In this instructor-led live training held in US, participants will acquire the skills to utilize Python and Spark for the analysis of big data, supported by practical, hands-on exercises.

Upon completion, participants will be equipped to:

• Apply Spark and Python for the analysis of Big Data for government purposes.
• Execute exercises that replicate real-world operational scenarios.
• Utilize specialized tools and methodologies for big data analysis using PySpark.

Stratio is a comprehensive data management platform that consolidates big data, artificial intelligence, and governance frameworks into a unified solution. Its Rocket and Intelligence modules facilitate rapid data exploration, transformation, and advanced analytics for enterprise-scale operations.

This instructor-led training, available online or in-person, is designed for intermediate-level data professionals seeking to proficiently utilize the Rocket and Intelligence modules within Stratio. The curriculum emphasizes the application of PySpark, with specific focus on iterative structures, user-defined functions, and complex data logic for government and public sector applications.

Upon completion of this training, participants will demonstrate the ability to:

• Operate and navigate the Stratio platform utilizing the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analytical processes.
• Employ iterative logic and conditional structures to manage data workflows and feature engineering tasks.
• Develop and maintain user-defined functions (UDFs) to support reusable data operations in PySpark.

Training Delivery Format

• Interactive instruction and facilitated discussion.
• Extensive practice exercises and applied scenarios.
• Practical implementation within a live laboratory environment.

Curriculum Customization Options

• To request a customized training program for this course, please contact the administration for arrangements.