Curriculum

13 Chapters · 13 Lessons

  1. 1
    1. (Included in full purchase)

      Free Preview

  2. 2
    1. (Included in full purchase)

      Real-Time Analytics Landscape and Use Cases

  3. 3
    1. (Included in full purchase)

      Apache Spark Fundamentals (with a Streaming Mindset)

  4. 4
    1. (Included in full purchase)

      Structured Streaming

  5. 5
    1. (Included in full purchase)

      Deep Dive into Sources and Sinks

  6. 6
    1. (Included in full purchase)

      Windowed and Stateful Operations

  7. 7
    1. (Included in full purchase)

      Writing Streaming Queries with Spark SQL

  8. 8
    1. (Included in full purchase)

      Low-Latency Streaming with Spark Real-Time Mode

  9. 9
    1. (Included in full purchase)

      Machine Learning for Streaming Applications

  10. 10
    1. (Included in full purchase)

      Monitoring, Debugging, and Performance Tuning

  11. 11
    1. (Included in full purchase)

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

  12. 12
    1. (Included in full purchase)

      End-to-End Real-Time Analytics Project

  13. 13
    1. (Included in full purchase)

      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.