Master end-to-end data engineering on Azure Databricks. From data ingestion and Delta Lake to CI/CD and real-time streaming, build secure, scalable, and performant data solutions with Spark, Unity Catalog, and ML tools.
Key Features
- Build scalable data pipelines using Apache Spark and Delta Lake
- Automate workflows and manage data governance with Unity Catalog
- Learn real-time processing and structured streaming with practical use cases
- Implement CI/CD, DevOps, and security for production-ready data solutions
- Explore Databricks-native ML, AutoML, and Generative AI integration
Book Description
"Data Engineering with Azure Databricks" is your essential guide to building scalable, secure, and high-performing data pipelines using the powerful Databricks platform on Azure. Designed for data engineers, architects, and developers, this book demystifies the complexities of Spark-based workloads, Delta Lake, Unity Catalog, and real-time data processing.
Beginning with the foundational role of Azure Databricks in modern data engineering, you’ll explore how to set up robust environments, manage data ingestion with Auto Loader, optimize Spark performance, and orchestrate complex workflows using tools like Azure Data Factory and Airflow.
The book offers deep dives into structured streaming, Delta Live Tables, and Delta Lake’s ACID features for data reliability and schema evolution. You’ll also learn how to manage security, compliance, and access controls using Unity Catalog, and gain insights into managing CI/CD pipelines with Azure DevOps and Terraform.
With a special focus on machine learning and generative AI, the final chapters guide you in automating model workflows, leveraging MLflow, and fine-tuning large language models on Databricks. Whether you're building a modern data lakehouse or operationalizing analytics at scale, this book provides the tools and insights you need.
What you will learn
- Set up a full-featured Azure Databricks environment
- Implement batch and streaming ingestion using Auto Loader
- Optimize Spark jobs with partitioning and caching
- Build real-time pipelines with structured streaming and DLT
- Manage data governance using Unity Catalog
- Orchestrate production workflows with jobs and ADF
- Apply CI/CD best practices with Azure DevOps and Git
- Secure data with RBAC, encryption, and compliance standards
- Use MLflow and Feature Store for ML pipelines
- Build generative AI applications in Databricks
Who this book is for
This book is for data engineers, solution architects, cloud professionals, and software engineers seeking to build robust and scalable data pipelines using Azure Databricks. Whether you're migrating legacy systems, implementing a modern lakehouse architecture, or optimizing data workflows for performance, this guide will help you leverage the full power of Databricks on Azure. A basic understanding of Python, Spark, and cloud infrastructure is recommended.
Table of Contents
- The Role of Azure Databricks in Modern Data Engineering
- Setting up an End-To-End Azure Databricks Environment
- Data Ingestion Strategies for Azure Databricks
- Data Engineering with Apache Spark
- Building Real-Time Data Pipelines
- Working with Delta Lake: ACID Transactions and Schema Evolution
- Automating Data Systems with Lakeflow Spark Declarative Pipelines
- Orchestrating Data Workflows: From Notebooks to Production
- CI/CD and DevOps for Azure Databricks
- Optimizing Query Performance and Cost Management
- Security, Compliance, and Data Governance
- Machine Learning and AI on Databricks
Dmitry Foshin is a Business Intelligence team leader focused on delivering business insights to the management team through data engineering, analytics, and visualization. He has led and executed complex full-stack BI solutions (from ETL processes to building DWHs and reporting) using Azure technologies, Data Lake, Data Factory, Data Bricks, MS Office 365, Power BI, and Tableau. He has also successfully launched numerous data analytics projects – both on-premises and in the cloud – that help achieve corporate goals for international FMCG companies, banks, and manufacturing companies.
Dmitry Anoshin is a data-centric technologist and a recognized expert in building and implementing big data and analytics solutions. He has a successful track record of implementing business and digital intelligence projects across retail, finance, marketing, and e-commerce. Dmitry possesses in-depth knowledge of digital/business intelligence, ETL, data warehousing, and big data technologies. He has extensive experience in data integration and is proficient in various data warehousing methodologies. Dmitry has consistently exceeded project expectations across the financial, machine tool, and retail industries. He has completed a number of multinational full BI/DI solution life cycle implementation projects. With expertise in data modeling, Dmitry also has a background and business experience in multiple relational databases, OLAP systems, and NoSQL databases. He is also an active speaker at data conferences and helps people to adopt cloud analytics.
Tonya Chernyshova is an experienced Data Engineer with over 10 years in the field, including time at Amazon. Specializing in Data Modeling, Automation, Cloud Computing (AWS and Azure), and Data Visualization, she has a strong track record of delivering scalable, maintainable data products. Her expertise drives data-driven insights and business growth, showcasing her proficiency in leveraging cloud technologies to enhance data capabilities.
Sergii Volodarskyi is a Data Engineer working daily on the Databricks ecosystem, across both platform and product data engineering. His expertise spans the full spectrum of modern data platform delivery, from designing lakehouse architectures and building CI/CD pipelines to extracting data from APIs and shipping analytical products that drive business decisions. This book is a reflection of experience and best practices built up across real projects. He actively shares his knowledge with the engineering community and is a builder with a deep interest in the intersection of data, AI, and software engineering.
