Data Engineering with Azure Databricks
Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks
Created by Dmitry Foshin, Tonya Chernyshova, Dmitry Anoshin and 1 more
Master the essentials of building robust data pipelines and analytics solutions using Azure Databricks. Explore real-time data processing, governance, and machine learning workflows to create scalable, secure, and high-performing data platforms. Gain practical skills to make Databricks your central data engineering tool.
Packt | Apr 2026 | 412 min
What You Will Learn
You will work through practical scenarios that guide you from setting up Databricks environments to building and optimizing data pipelines. Each step introduces hands-on techniques for data ingestion, transformation, and real-time processing, while also covering governance, security, and automation. By the end, you'll be able to operationalize analytics and machine learning at scale.
Key Features
- Design and optimize scalable data pipelines with Spark and Delta Lake on Azure
- Implement real-time streaming and automate workflows for efficient analytics
- Apply data governance, security, and ML workflows using Unity Catalog and MLflow
Target Audience
Ideal for data engineers, architects, and cloud professionals who want to design and manage scalable data solutions on Azure. If you have a basic understanding of Python, Spark, and cloud infrastructure, and are looking to modernize data workflows or implement a lakehouse architecture, you'll find actionable guidance and best practices here.
