PySpark Training in Argentina

This instructor-led live training in Argentina explores the Stratio platform, with a specific focus on integrating the Rocket and Intelligence modules with PySpark. Participants will gain mastery over data ingestion, transformation, and advanced analytics, while developing practical skills in implementing loops, UDFs, and enterprise-grade data workflows.

In this live, instructor-led training in Argentina, participants will learn to combine Python and Spark for big data analysis through hands-on exercises.

By the end of the session, participants will be able to:

• Use Spark with Python to analyze Big Data.
• Complete exercises that simulate real-world cases.
• Employ diverse tools and techniques for big data analysis using PySpark.

This instructor-led, live training (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. Attendees will gain insight into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently process large datasets by applying distributed computing principles.

Designed as a three-day immersive experience, this course empowers learners to construct and fine-tune high-efficiency data-processing pipelines utilizing PySpark, Pandas, and Polars within Kubernetes-based architectures.

Learners will cultivate a practical command of how Spark applications function on Kubernetes, gaining insight into how specific configuration choices at the application level directly impact performance, scalability, resource utilization, and operational costs. Key optimization domains covered include executor sizing, memory management, dynamic allocation, partitioning logic, shuffle dynamics, mitigating small-file issues, and streamlined Parquet processing.

The curriculum also tackles prevalent challenges associated with Pandas, such as memory constraints and out-of-memory failures, while presenting Polars as a high-performance alternative for specific workloads. Through interactive, hands-on exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration strategies, and implement optimization techniques in realistic ETL and machine learning contexts.

Throughout the course, the primary focus remains on practical decision-making: mastering the ability to identify bottlenecks, select the most suitable tool, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.