Real-Time Analytics with Apache Spark - by Subhadip Chand & Harsha Pasala (Paperback)

Book Synopsis

Turn Data in Motion into Decisions in Real

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