Harsha Pasala / Subhadip Chand
Librería Samer Atenea
Kálamo Books
Librería Elías (Asturias)
Librería Kolima (Madrid)
Librería Proteo (Málaga)
Turn Data in Motion into Decisions in RealBook DescriptionThe Next Generation of Data Platforms Will Be Real-Time, Intelligent, and Always OnReal-time Analytics with Apache Spark is your complete, comprehensive guide to building production-grade streaming systems using Apache Spark Structured Streaming on the Databricks platform, from first principles to enterprise-scale deployment.You begin with Spark fundamentals and streaming concepts, then progressively advance through windowed aggregations, stateful processing with transformWithState, stream-stream joins, and the new Real-time Mode for sub-second latency. Every chapter combines clear explanations with production-ready code, preparing you to handle real-world challenges including late data, state management, and performance tuning across Kafka, Kinesis, Event Hubs, and Auto Loader.The final section teaches you to think like a production engineer by packaging pipelines with Declarative Automation Bundles, automating deployments with CI/CD, integrating ML inference into streaming workflows, and building monitoring dashboards with custom alerts. By the end of the book, you will have a proven blueprint for delivering scalable, fault-tolerant streaming solutions on Apache Spark and Databricks.What you will learn● Build fault-tolerant streaming pipelines with exactly-once guarantees on Apache Spark.● Apply windowed aggregations, watermarks, and stateful processing for real-time data workflows.● Ingest streaming data from Kafka, Kinesis, Event Hubs, and Auto Loader at scale.● Deploy streaming pipelines using Declarative Automation Bundles and CI/CD on Databricks.● Integrate real-time ML inference into production streaming data workflows with confidence.● Monitor, debug, and tune streaming jobs for production performance and operational reliability.Table of Contents1. Real-Time Analytics Landscape and Use Cases2. Apache Spark Fundamentals (with a Streaming Mindset)3. Structured Streaming4. Deep Dive into Sources and Sinks5. Windowed and Stateful Operations6. Writing Streaming Queries with Spark SQL7. Low-Latency Streaming with Spark Real-Time Mode8. Machine Learning for Streaming Applications9. Monitoring, Debugging, and Performance Tuning10. Packaging, Orchestration, and CI/CD Using Declarative Automation Bundles.11. End-to-End Real-Time Analytics Project Index
