Databricks and PySpark for Data Engineering Training Course

This live training on Germany explores machine learning with Azure Databricks in the finance sector, with a focus on constructing, evaluating, and deploying models. Participants will learn to utilize MLflow, integrate models into scalable pipelines, and apply machine learning for anomaly detection and trend forecasting within regulated environments.

This live, instructor-led training, delivered either online or on-site, is designed for data scientists and developers seeking to set up, deploy, and manage data analytics solutions using Databricks.

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

• Set up and configure Databricks.
• Understand the collaboration between Databricks and Apache Spark.
• Learn methods for loading and transforming data in Databricks.

This five-day intensive in Germany guides 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, enabling you to build optimized, end-to-end pipelines for modern data engineering.

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

This training delivers a practical introduction to developing scalable data processing and machine learning workflows using PySpark. Participants will understand how Apache Spark fits into modern big data ecosystems and how to leverage distributed computing principles for efficient large-scale data processing.

This three-day, hands-on program is dedicated to constructing and refining high-efficiency data-processing workloads that leverage PySpark, Pandas, and Polars within Kubernetes environments.

Learners will gain a practical grasp of how Spark applications function on Kubernetes, specifically how application-level configuration choices impact performance, scalability, resource utilization, and overall costs. The curriculum addresses critical optimization areas, such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, resolving small-file issues, and efficient Parquet processing.

Additionally, the course tackles frequent challenges encountered with Pandas, including memory constraints and out-of-memory failures, while introducing Polars as a high-performance alternative for specific data-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.

Throughout the course, the primary focus remains on practical decision-making: mastering the ability to identify performance bottlenecks, select the right tools, configure Spark effectively, and balance high performance against infrastructure resource consumption and costs.

In this instructor-led live training in Germany, participants will learn how to leverage Python and Spark together to analyze big data, supported by practical hands-on exercises.

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

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

This live, instructor-led training in Germany explores the Stratio platform, specifically highlighting the integration of Rocket and Intelligence modules with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, while developing practical skills in handling loops, UDFs, and enterprise-grade data workflows.