PySpark Training in Brazil

This instructor-led live training in Brazil explores the Stratio platform, with a specific focus on the Rocket and Intelligence modules integrated with PySpark. Learners will acquire proficiency in data ingestion, transformation, and advanced analytics, developing practical competencies in loops, UDFs, and enterprise-grade data workflows.

In this instructor-led, live training in Brazil, attendees will discover how to effectively combine Python and Spark for big data analysis while participating in hands-on exercises.

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

• Utilize Spark with Python to conduct Big Data analysis.
• Solve problems through exercises modeled after real-world cases.
• Employ diverse tools and methods for big data analysis using PySpark.

This instructor-led, live training (available online or onsite) is aimed at data engineers, data analysts, and data professionals who wish to use Databricks and PySpark to build scalable data pipelines and migrate existing SQL workflows.

This training offers a hands-on introduction to creating scalable data processing and Machine Learning workflows using PySpark. Participants will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and how to efficiently manage large datasets by leveraging distributed computing principles.

This intensive three-day workshop is dedicated to constructing and refining high-efficiency data processing workflows within Kubernetes environments, utilizing PySpark, Pandas, and Polars.

Attendees will gain a practical grasp of Spark application execution mechanics on Kubernetes, focusing on how specific configuration choices impact performance, scalability, resource usage, and operational costs. Key optimization topics include executor sizing, memory management, dynamic allocation, partitioning methods, shuffle operations, mitigating the small-file issue, and optimizing Parquet processing.

The curriculum also tackles typical obstacles encountered with Pandas, such as memory constraints and out-of-memory exceptions, while introducing Polars as a high-speed alternative for specific data tasks. Through practical exercises, learners will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and implement optimization techniques in realistic ETL and machine learning contexts.

Throughout the course, the primary focus remains on practical decision-making: mastering the identification of bottlenecks, selecting the right tools, configuring Spark for maximum efficiency, and balancing performance against infrastructure resource consumption and cost.