Advanced Analytics with Spark

by Sandy Ryza, Uri Laserson, Sean Owen

In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications. With this book, you

Genres: Computer Science, Programming, Technology, Computers, Ebooks, Technical, Software

274 pages, Paperback

First published April 2, 2015

Reviews

[Taras Fedorov]

Хороша книга для людина, яка хоче заглянути у BigData і зробити це на практиці.

Зрозумілі та конкретні приклади та покрокова інструкція.

[Liam Bui]

The book focuses more on use cases and less on technical aspects. It is great book for readers who wish to explore the use of Spark in their business/domains rather than seeking to understand detailed technical aspects of Spark and machine learning algorithm.
+ Practical examples in multiple domains (financial risk, bioinformatics, transportation, etc)
+ Full code provided with detailed instruction and explanation
+ Introduction of Spark analytics functionalities
- Brief explanation of machine learning concept/algorithm

[Madhavan]

Very Interesting book. The examples and code walkthroughs are great for those who want to enter the world of Scala + Spark. Recommend the book.

[TN]

It is a well written book. I found the chapters on PySpark and MLib useful. However, the topics on genomic data and neuroimaging weren't quite consistent and probably will require more attention.

[Frank Palardy]

Good examples.

[Delhi Irc]

DL027817 GG6

[Aboud Akhdir]

Great one, very thorough.