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
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.
