Modern data engineering is no longer just about writing SQL or moving files from one system to another. Today's data engineers need to build pipelines that are scalable, reliable, optimized, secure, observable, and ready for production.
PySpark & Databricks Data Engineering provides a practical, structured path from foundational data engineering concepts to advanced enterprise-level data platforms.
Starting with Python, Spark, and PySpark fundamentals, the book progressively takes you into advanced transformations, performance optimization, Delta Lake, incremental processing, CDC, Structured Streaming, Auto Loader, Databricks Workflows, Unity Catalog, security, governance, system design, and production troubleshooting.
Inside this book, you will learn: Data engineering fundamentals and architecturePython concepts required for data engineeringPySpark DataFrames and Spark SQLTransformations, actions, joins, aggregations, and window functionsComplex and nested data processingSpark partitioning and shuffleBroadcast joins and data-skew handlingSpark performance optimizationDatabricks Lakehouse architectureDelta Lake and ACID transactionsMERGE, UPDATE, DELETE, and time travelIncremental data processingChange Data Capture (CDC)Structured StreamingAuto LoaderCheckpointing and watermarksDatabricks Workflows and orchestrationData quality and production reliabilityUnity CatalogSecurity and access controlData lineage and governanceData products and enterprise architectureMonitoring, observability, and cost optimizationCI/CD and testingDisaster recovery conceptsEnd-to-end data engineering projectsProduction troubleshootingDatabricks system-design interviewsPySpark coding challenges