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Artículo: AMZ-8169646103
Real-Time Analytics with Apache Spark: Master Structured Streaming, Kafka, Databricks, Real-Time Data Pipelines, Stateful Processing, and Production-Scale Stream Engineering (English Edition)
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Paperback
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En stock
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0.63 kg
0.63 kg
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Sí
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Nuevo
Nuevo
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Amazon
Amazon
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USA
USA
Sobre este producto
- Turn Data in Motion into Decisions in Real Key Features ● Get a free one-month digital subscription to www.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. Table of Contents 1. Real-Time Analytics Landscape and Use Cases 2. Apache Spark Fundamentals (with a Streaming Mindset) 3. Structured Streaming 4. Deep Dive into Sources and Sinks 5. Windowed and Stateful Operations 6. Writing Streaming Queries with Spark SQL 7. Low-Latency Streaming with Spark Real-Time Mode 8. Machine Learning for Streaming Applications 9. Monitoring, Debugging, and Performance Tuning 10. Packaging, Orchestration, and CI/CD Using Declarative Automation Bundles. 11. End-to-End Real-Time Analytics Project Index
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