PySpark Training in Slovakia

This instructor-led live training in Slovakia covers the Stratio platform, focusing on the Rocket and Intelligence modules with PySpark. Participants will master data ingestion, transformation, and advanced analytics, gaining practical skills in loops, UDFs, and enterprise data workflows.

In this instructor-led live training held in Slovakia, participants will master the use of Python and Spark for big data analysis through immersive, hands-on exercises.

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

• Apply Spark in conjunction with Python to analyze large-scale data sets.
• Complete exercises that reflect practical, real-world applications.
• Leverage diverse tools and methodologies for big data analysis using PySpark.

This instructor-led live training (online or onsite) is designed for 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 developing scalable data processing and Machine Learning workflows with PySpark. Participants will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process large datasets efficiently by applying distributed computing principles.

This intensive three-day course is dedicated to the construction and refinement of high-efficiency data processing pipelines utilizing PySpark, Pandas, and Polars within Kubernetes ecosystems.

Learners will gain hands-on insight into the execution mechanics of Spark applications on Kubernetes, specifically examining how application-level configuration choices dictate performance, scalability, resource utilization, and operational costs. The curriculum delves into critical optimization domains, such as executor sizing, memory management, dynamic allocation, partitioning methodologies, shuffle dynamics, mitigation of small-file issues, and the efficient handling of Parquet data.

Furthermore, the course tackles prevalent challenges associated with Pandas, such as memory constraints and out-of-memory failures, while introducing Polars as a high-performance alternative for specific processing tasks. Through practical 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.

The core focus remains on practical decision-making: mastering the identification of bottlenecks, selecting the optimal tooling, configuring Spark for maximum efficiency, and balancing performance against infrastructure resource consumption and cost.