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.

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Spis treści

Data Engineering with Azure Databricks. Design, build, and optimize scalable data pipelines and analytics solutions with Azure Databricks eBook -- spis treści

• 1. The role of Azure Databricks in modern data engineering
• 2. Setting up an end-to-end Azure Databricks environment
• 3. Data ingestion strategies for Azure Databricks
• 4. Deep dive into Apache Spark on Azure Databricks
• 5. Streaming architectures with structured streaming
• 6. Working with Delta Lake: ACID transactions & schema evolution
• 7. Automating data pipelines with Delta Live Tables (DLT)
• 8. Orchestrating data workflows: from notebooks to production
• 9. CI/CD and DevOps for Azure Databricks
• 10. Optimizing query performance and cost management
• 11. Security, compliance, and data governance
• 12. Machine learning, AutoML, and generative AI in Databricks