Update README.md

README.md

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 * [Fundamentals of Stream Processing: Application Design, Systems, and Analytics](http://www.cambridge.org/us/academic/subjects/engineering/communications-and-signal-processing/fundamentals-stream-processing-application-design-systems-and-analytics) - This comprehensive, hands-on guide combining the fundamental building blocks and emerging research in stream processing is ideal for application designers, system builders, analytic developers, as well as students and researchers in the field.
 * [Stream Data Processing: A Quality of Service Perspective](http://www.springer.com/us/book/9780387710020) - Presents a new paradigm suitable for stream and complex event processing.
 * [Unified Log Processing](https://www.manning.com/books/event-streams-in-action) - Unified Log Processing is a practical guide to implementing a unified log of event streams (Kafka or Kinesis) in your business
+* [Kafka Streams in Action](https://www.manning.com/books/kafka-streams-in-action) - Kafka Streams in Action teaches you everything you need to know to implement stream processing on data flowing into your Kafka platform, allowing you to focus on getting more from your data without sacrificing time or effort.
+* [Big Data](https://www.manning.com/books/big-data) - Big Data teaches you to build big data systems using an architecture that takes advantage of clustered hardware along with new tools designed specifically to capture and analyze web-scale data.
+* [Spark in Action](https://www.manning.com/books/spark-in-action) - Spark in Action teaches you the theory and skills you need to effectively handle batch and streaming data using Spark. Fully updated for Spark 2.0.
 
 #### Distributed systems
 * [Distributed Systems for fun and profit](http://book.mixu.net/distsys/) – Theory of distributed systems. Include parts about time and ordering, replication and impossibility results.