Real-Time Analytics with Apache Spark English | 2026 | ISBN: 8169646103 | 371 pages | True EPUB | 9.97 MB Turn Data in Motion into Decisions in Real
Key Features ● Get a free one-month digital subscription towww.avaskillshelf.com. ● Master Spark Structured Streaming from windowed aggregations and stateful processing to sub-second latency. ● Build production ingestion pipelines using Kafka, Kinesis, Event Hubs, and Auto Loader at scale. ● Deploy, monitor, and integrate ML inference into streaming workflows using CI/CD and Declarative Automation Bundles. Book Description The Next Generation of Data Platforms Will Be Real-Time, Intelligent, and Always On Real-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. Who is this book for? This book is for data engineers, analytics engineers, and architects who want to build production-grade streaming pipelines on Apache Spark and Databricks. Basic Python proficiency and familiarity with SQL and batch-mode Spark are assumed; no prior streaming experience is required.
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