Books DATABRICKS AND APACHE SPARK IN ACTION A Practical Guide to Building Scalable Data Pipelines and Advanced Analytics Workflows By Jeffrey Tromp

"Databricks and Apache Spark in Action: A Practical Guide to Building Scalable Data Pipelines and Advanced Analytics Workflows" by Jeffrey Tromp serves as a hands-on manual for leveraging Databricks' unified platform atop Apache Spark to engineer robust data systems. It demystifies Spark's distributed computing for ETL pipelines, real-time analytics, and ML workflows, using Python, Scala, and SQL examples tailored to cloud environments.​

Core Coverage

The book progresses from Spark fundamentals—RDDs, DataFrames, lazy evaluation—to Databricks-specific tools like Delta Lake for ACID transactions, Unity Catalog for governance, and workflows for orchestration. Practical labs cover ingestion from diverse sources, transformations via Spark SQL/MLlib, and optimization techniques like caching, partitioning, and AQE for production-scale performance.​

Advanced Workflows

Sections detail scalable pipelines with Structured Streaming, feature stores, and end-to-end ML ops, including model training on massive datasets and deployment via MLflow. It addresses common pitfalls like shuffle spills and executor tuning, empowering data engineers to build resilient systems.