Data Engineering with Azure Databricks

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 - Deep dive into Apache Spark on Azure Databricks - Streaming architectures with structured streaming - Working with Delta Lake: ACID transactions & schema evolution - Automating data pipelines with Delta Live Tables (DLT) - 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, AutoML, and generative AI in Databricks