Databricks and PySpark for Data Engineering Training Course

This live training in Latvia covers machine learning with Azure Databricks for finance, focusing on building, evaluating, and deploying models. Participants learn to use MLflow, integrate models into scalable pipelines, and apply ML to detect anomalies and forecast trends in regulated environments.

This live, instructor-led training, conducted in Latvia (either online or on-site), is designed for data scientists and developers seeking to build, deploy, and administer data analytics solutions using Databricks.

By the conclusion of this program, participants will have the capability to:

• Set up and configure the Databricks platform.
• Understand the synergy between Databricks and Apache Spark.
• Apply methods for loading and transforming data within Databricks.

This five-day intensive in Latvia assists intermediate tech managers in migrating stored procedures to a Databricks Lakehouse. Through hands-on labs, you will master Spark, Delta Lake, and Unity Catalog to build optimized, end-to-end pipelines for modern data engineering.

This instructor-led Databricks course in Latvia provides finance professionals with practical skills in data analysis, visualization, and teamwork. Participants will learn how to cleanse financial data, construct KPI dashboards, and optimize queries to support real-time reporting and performance monitoring.

This program provides a practical foundation for constructing scalable data processing and Machine Learning workflows with PySpark. It teaches participants how Apache Spark integrates into modern Big Data ecosystems and how to utilize distributed computing to process large datasets efficiently.

Over the course of three intensive days, this practical training programme centres on the development and refinement of high-efficiency data processing workflows leveraging PySpark, Pandas, and Polars within Kubernetes-based ecosystems.

Learners will cultivate a hands-on grasp of Spark application execution on Kubernetes, specifically examining how configuration decisions at the application level impact performance, scalability, resource utilization, and operational costs. Key optimisation topics include sizing executors, managing memory allocation, dynamic allocation, partitioning methodologies, shuffle dynamics, mitigating small-file issues, and enhancing Parquet processing efficiency.

Additionally, the course tackles frequent obstacles encountered in Pandas usage, such as memory constraints and out-of-memory exceptions, while introducing Polars as a high-performance alternative for specific data processing tasks. Through practical exercises, participants will learn to identify performance and memory bottlenecks, evaluate various configuration strategies, and implement optimisation techniques in realistic ETL and machine learning contexts.

The primary focus remains on practical decision-making: gaining the ability to pinpoint bottlenecks, select the most suitable tool, configure Spark for peak efficiency, and strike a balance between performance and infrastructure resource consumption and cost.

Join this instructor-led, live training in Latvia, where you will discover how to leverage the combined power of Python and Spark for big data analysis through practical, hands-on exercises.

Upon completion, participants will be equipped to:

• Apply Spark and Python synergistically to process and analyze large datasets.
• Engage with exercises that replicate real-world scenarios.
• Leverage various tools and methodologies for big data analytics using PySpark.

This live, instructor-led training in Latvia delves into the Stratio platform, with a specific focus on integrating the Rocket and Intelligence modules with PySpark. Participants will gain proficiency in data ingestion, transformation, and advanced analytics, while developing practical skills in loop structures, user-defined functions (UDFs), and enterprise-level data workflows.