PySpark Training in Thailand

This instructor-led live training in Thailand delves into the Stratio platform, specifically highlighting the Rocket and Intelligence modules integrated with PySpark. Attendees will gain proficiency in data ingestion, transformation, and advanced analytics, while developing practical expertise in loops, UDFs, and enterprise-grade data workflows.

In this live, instructor-led training in Thailand, participants will acquire the ability to use Python and Spark together for big data analysis, reinforced by hands-on exercises.

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

• Utilize Spark and Python for Big Data analysis.
• Complete exercises that reflect real-world applications.
• Employ diverse tools and techniques for big data analysis using PySpark.

This live, instructor-led training, delivered online or onsite, targets data engineers, analysts, and professionals looking to utilize Databricks and PySpark to develop scalable data pipelines and transition existing SQL workflows.

This training delivers a practical entry point into developing scalable data processing and Machine Learning workflows using PySpark. Learners will understand how Apache Spark fits into modern Big Data ecosystems and how to leverage distributed computing to process large datasets efficiently.

This intensive three-day workshop is designed to help you build and fine-tune high-performance data-processing pipelines using PySpark, Pandas, and Polars within Kubernetes ecosystems.

Attendees will gain a practical grasp of Spark execution mechanics on Kubernetes, learning how application-level configuration impacts performance, scalability, resource efficiency, and operational costs. The curriculum addresses critical optimization strategies, such as executor sizing, memory management, dynamic allocation, partitioning techniques, shuffle optimization, and efficient Parquet handling, while also tackling the small-file problem.

The program also explores common pitfalls associated with Pandas, such as memory constraints and out-of-memory errors, and positions Polars as a high-performance alternative for specific workload scenarios. Through hands-on lab exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate different configuration strategies, and apply optimization techniques to real-world ETL and machine learning projects.

The core focus of the training is on practical decision-making: mastering the skills to identify performance bottlenecks, select the right tools, configure Spark effectively, and strike the ideal balance between processing performance and infrastructure resource consumption and cost.