Curriculum

13 Chapters · 13 Lessons

  1. 1

    Free Preview

    1. (Included in full purchase)

      Free Preview

  2. 2

    Chapter 1: Real-Time Analytics Landscape and Use Cases

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      Real-Time Analytics Landscape and Use Cases

  3. 3

    Chapter 2: Apache Spark Fundamentals (with a Streaming Mindset)

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      Apache Spark Fundamentals (with a Streaming Mindset)

  4. 4

    Chapter 3: Structured Streaming

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      Structured Streaming

  5. 5

    Chapter 4: Deep Dive into Sources and Sinks

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      Deep Dive into Sources and Sinks

  6. 6

    Chapter 5: Windowed and Stateful Operations

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      Windowed and Stateful Operations

  7. 7

    Chapter 6: Writing Streaming Queries with Spark SQL

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      Writing Streaming Queries with Spark SQL

  8. 8

    Chapter 7: Low-Latency Streaming with Spark Real-Time Mode

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      Low-Latency Streaming with Spark Real-Time Mode

  9. 9

    Chapter 8: Machine Learning for Streaming Applications

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      Machine Learning for Streaming Applications

  10. 10

    Chapter 9: Monitoring, Debugging, and Performance Tuning

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      Monitoring, Debugging, and Performance Tuning

  11. 11

    Chapter 10: Packaging, Orchestration, and CI/CD Using Declarative Automation Bundles.

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      Packaging, Orchestration, and CI/CD Using Declarative Automation Bundles.

  12. 12

    Chapter 11: End-to-End Real-Time Analytics Project

    1. (Included in full purchase)

      End-to-End Real-Time Analytics Project

  13. 13

    Index

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      Index

About the Course

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.

About the Author

Subhadip Chanda and Harsha Pasala are experts in real-time data engineering, specializing in scalable Spark and Databricks streaming architectures. Combining deep production experience with practical design insight, they guide readers beyond prototypes to build resilient, low-latency, and future-ready analytics pipelines that operate reliably at enterprise scale.