Databricks and PySpark for Data Engineering Training Course

This live training in Thailand focuses on applying machine learning with Azure Databricks in the financial sector, emphasizing the construction, assessment, and deployment of models. Participants will gain expertise in utilizing MLflow, integrating models into scalable pipelines, and leveraging ML capabilities to detect anomalies and predict trends within regulated environments.

This live, instructor-led training is conducted in Thailand (via online or onsite sessions) and is specifically designed for data scientists and developers aiming to implement, deploy, and administer data analytics solutions with Databricks.

By the conclusion of this session, participants will have the capability to:

• Set up and configure the Databricks environment.
• Understand the synergy between Databricks and Apache Spark.
• Learn techniques for loading and transforming data in Databricks.

This five-day intensive in Thailand supports intermediate tech managers in migrating stored procedures to a Databricks Lakehouse. Through hands-on labs, you will master Spark, Delta Lake, and Unity Catalog to construct optimized, end-to-end pipelines for modern data engineering.

This instructor-led Databricks training in Thailand empowers finance professionals with practical skills in data analysis, visualization, and teamwork. Participants will learn to process financial data, develop KPI dashboards, and refine queries to enhance real-time reporting and performance oversight.

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.

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 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.