In this chapter, we introduced the topic of machine learning. You also learned how to run K-means clustering algorithms over Storm using Trident-ML and then use the generated model to predict the category of data using DRPC.

Although we used Trident-ML in this chapter, there are other machine learning packages also available for Storm. Storm.pattern (GitHub repository: https://github.com/quintona/storm-pattern) is one such library that can import models from other non-Storm packages, such as R, Weka, and so on.

With this, we come to the end of this book. Through the course of this book, we have come a long way from taking our first steps with Apache Storm to developing real-world applications with it. Here, we would like to summarize everything that we learned.

We introduced you to the basic concepts and components of Storm and covered how we can write and deploy/run the topology in the local and clustered modes. We also walk through the basic commands of Storm and cover how we can modify the parallelism of the Storm topology in runtime. We also dedicated an entire chapter to monitoring Storm, which is an area often neglected during development, but is a critical part of any production setting. You also learned about Trident, which is an abstraction over the low-level Storm API to develop more complex topologies and maintain the application state.

No enterprise application can be developed in a single technology, and so our next step was to see how we could integrate Storm with other Big Data tools and technologies. We saw specific implementation of Storm with Kafka, Hadoop, HBase, and Redis. Most of the Big Data applications use Ganglia as a centralized monitoring tool. Hence, we also covered how we could monitor the Storm cluster through JMX and Ganglia.

You also learned about various patterns to integrate diverse data sources with Storm. Finally, in Chapter 8, Log Processing with Storm, and this chapter, we implemented two case studies in Apache Storm, which can serve as a starting point for developing more complex applications.

We hope that reading this book has been a fruitful journey for you, and that you developed a basic understanding of Storm and, in general, various aspects of developing a real-time stream processing application. Apache Storm is turning into a de facto standard for stream processing, and we hope that this book will act as a catalyst for you to jumpstart the exciting journey of building a real-time stream processing applications.