Preface

Welcome to the second edition of Clojure Data Analysis Cookbook! It seems that books become obsolete almost as quickly as software does, so here we have the opportunity to keep things up-to-date and useful.

Moreover, the state of the art of data analysis is also still evolving and changing. The techniques and technologies are being refined and improved. Hopefully, this book will capture some of that. I've also added a new chapter on how to work with unstructured textual data.

In spite of these changes, some things have stayed the same. Clojure has further proven itself to be an excellent environment to work with data. As a member of the lisp family of languages, it inherits a flexibility and power that is hard to match. The concurrency and parallelization features have further proven themselves as great tools for developing software and analyzing data.

Clojure's usefulness for data analysis is further improved by a number of strong libraries. Incanter provides a practical environment to work with data and perform statistical analysis. Cascalog is an easy-to-use wrapper over Hadoop and Cascading. Finally, when you're ready to publish your results, ClojureScript, an implementation of Clojure that generates JavaScript, can help you to visualize your data in an effective and persuasive way.

Moreover, Clojure runs on the Java Virtual Machine (JVM), so any libraries written for Java are available too. This gives Clojure an incredible amount of breadth and power.

I hope that this book will give you the tools and techniques you need to get answers from your data.