Databricks and PySpark for Data Engineering Training Course

This live training on Italy explores machine learning with Azure Databricks in the context of finance. It focuses on the complete lifecycle of building, evaluating, and deploying models. Participants will learn to utilize MLflow, integrate models into scalable pipelines, and apply machine learning techniques to detect anomalies and forecast trends within regulated environments.

This instructor-led, live training in Italy (online or on-site) is aimed at data scientists and developers who wish to set up, deploy, and manage data analytics solutions using Databricks.

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

• Set up and configure Databricks.
• Understand how Databricks and Apache Spark work together.
• Learn how to load and transform data in Databricks.

This five-day intensive in Italy supports 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 suitable for modern data engineering.

This instructor-led Databricks training in Italy provides finance professionals with practical skills in data analysis, visualization, and team collaboration. You will learn to clean financial data, construct KPI dashboards, and optimize queries for real-time reporting and performance monitoring.

This course offers a hands-on introduction to creating scalable data processing and Machine Learning workflows with PySpark. Attendees will discover how Apache Spark functions within contemporary Big Data ecosystems and learn to process extensive datasets efficiently by leveraging distributed computing principles.

This intensive three-day workshop is dedicated to engineering and refining high-performance data-processing pipelines that leverage PySpark, Pandas, and Polars within Kubernetes environments.

Learners will gain a hands-on grasp of how Spark applications operate on Kubernetes, focusing on how configuration choices at the application level directly impact performance, scalability, resource efficiency, and overall cost. The curriculum covers essential optimization topics such as executor sizing, memory management, dynamic allocation, partitioning strategies, shuffle mechanics, mitigating the small-file issue, and efficient handling of Parquet data.

The program also tackles frequent hurdles encountered with Pandas, such as memory constraints and out-of-memory errors, while introducing Polars as a high-speed alternative for specific data tasks. Through practical exercises, participants will learn to diagnose performance and memory bottlenecks, evaluate various configuration approaches, and apply optimization strategies to real-world ETL and machine learning use cases.

The core focus remains on practical decision-making: equipping professionals to pinpoint bottlenecks, choose the right tools, configure Spark effectively, and strike a balance between performance and infrastructure resource consumption and cost.

In this instructor-led, live training in Italy, participants will discover how to leverage Python and Spark together for big data analysis through practical, hands-on exercises.

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

• Analyze Big Data effectively using the integration of Spark and Python.
• Engage with exercises that simulate real-world scenarios.
• Apply various tools and techniques for big data analysis using PySpark.

This instructor-led live training on Italy explores the Stratio platform, with a primary focus on integrating PySpark with the Rocket and Intelligence modules. Participants will gain practical expertise in data ingestion, transformation, and advanced analytics, while mastering key techniques such as loops, UDFs, and enterprise-grade data workflows.