Databricks and PySpark for Data Engineering Training Course

This live training on Denmark is dedicated to applying machine learning in financial contexts using Azure Databricks, with a primary focus on the construction, evaluation, and deployment of models. Learners will acquire the skills to leverage MLflow, embed models within scalable pipelines, and utilize ML capabilities for anomaly detection and trend forecasting within regulated operational environments.

This instructor-led live training, delivered in Denmark (either online or on-site), is designed for data scientists and developers aiming to set up, deploy, and oversee data analytics solutions using Databricks.

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

• Establish and configure Databricks.
• Comprehend the interaction between Databricks and Apache Spark.
• Acquire the skills to load and transform data within Databricks.

This five-day intensive in Denmark 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 modern data engineering.

This instructor-led Databricks course in Denmark provides finance professionals with practical expertise in data analysis, visualization, and team collaboration. Participants will learn to process financial data, construct KPI dashboards, and optimize queries for real-time reporting and performance monitoring.

This training delivers a practical introduction to building scalable data processing and Machine Learning workflows using PySpark. Participants learn how Apache Spark operates within modern Big Data ecosystems and how to efficiently process large datasets using distributed computing principles.

This intensive three-day workshop is designed to equip professionals with the skills to construct and refine high-performance data-processing workloads leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Attendees will gain a functional understanding of how Spark applications operate on Kubernetes, focusing on how configuration choices at the application level directly impact performance, scalability, resource utilisation, and operational costs. The curriculum addresses critical optimisation domains, such as executor sizing, memory allocation, dynamic allocation, partitioning strategies, shuffle mechanics, the impact of small-file issues, and efficient Parquet handling.

Additionally, the course tackles frequent challenges associated with Pandas, such as memory constraints and out-of-memory errors, 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 approaches, and implement optimisation strategies for realistic ETL and machine learning use cases.

The core focus of the course remains on practical decision-making: mastering the ability to pinpoint bottlenecks, select the most appropriate tooling, configure Spark effectively, and achieve a balance between performance and infrastructure resource consumption and cost.

In this live, instructor-led training held in Denmark, participants will explore how to combine Python and Spark for big data analysis, supported by hands-on practical exercises.

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

• Apply Spark and Python to analyze big data effectively.
• Engage in practical exercises that simulate real-world conditions.
• Employ various tools and techniques for big data analysis using PySpark.

This live, instructor-led course in Denmark dives deep into the Stratio platform, specifically highlighting the integration of Rocket and Intelligence modules with PySpark. Learners will gain mastery over data ingestion, transformation, and advanced analytics, while building practical skills in loops, UDFs, and enterprise-grade data workflows.