Databricks and PySpark for Data Engineering Training Course

This live training on Hungary focuses on applying machine learning with Azure Databricks in the financial sector, emphasizing the creation, assessment, and deployment of models. Learners gain expertise in utilizing MLflow, integrating models into scalable pipelines, and applying ML techniques to detect anomalies and forecast trends within regulated environments.

This instructor-led, live training in Hungary (online or onsite) is designed for data scientists and developers seeking to configure, deploy, and administer data analytics solutions with Databricks.

By the conclusion of this training, participants will be capable of:

• Setting up and configuring Databricks.
• Understanding the integration of Databricks and Apache Spark.
• Learning methods to load and transform data in Databricks.

This five-day intensive program in Hungary assists intermediate tech managers in migrating stored procedures to a Databricks Lakehouse. Through hands-on labs, you will gain proficiency in Spark, Delta Lake, and Unity Catalog to construct optimized, end-to-end pipelines for contemporary data engineering.

This instructor-led Databricks training in Hungary provides finance professionals with practical skills in data analysis, visualization, and teamwork. Participants will learn to sanitize financial data, construct KPI dashboards, and enhance queries for real-time reporting and performance monitoring.

This course provides a practical foundation for building scalable data processing and Machine Learning workflows with PySpark. It covers the mechanics of Apache Spark within modern Big Data ecosystems and the efficient processing of large datasets through distributed computing.

Over three days, this practical course guides participants through the construction and refinement of high-performance data-processing pipelines within Kubernetes environments, leveraging PySpark, Pandas, and Polars.

Learners will gain a hands-on grasp of Spark's execution mechanics on Kubernetes, focusing on how specific configuration choices impact scalability, resource efficiency, and operational costs. Key topics include executor dimensioning, memory distribution, dynamic resource allocation, partition management, shuffle dynamics, mitigating the small-file issue, and optimising Parquet data handling.

The curriculum also tackles frequent Pandas pitfalls, such as memory constraints and out-of-memory exceptions, while introducing Polars as a high-speed alternative for targeted processing tasks. Through practical exercises, participants will learn to pinpoint performance bottlenecks and memory issues, evaluate various configuration approaches, and implement optimisation strategies in real-world ETL and machine learning contexts.

Throughout the session, the focus remains on practical decision-making: mastering the identification of bottlenecks, selecting the right tools, configuring Spark effectively, and striking a balance between performance and infrastructure costs.

In this instructor-led live training in Hungary, participants will discover how to combine Python and Spark to analyze big data while engaging in hands-on exercises.

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

• Utilize Spark alongside Python for big data analysis.
• Complete exercises that simulate real-world cases.
• Employ diverse tools and techniques for big data analysis using PySpark.

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