Advanced Analytics with Spark: Patterns for Learning from Data at Scale

by Sandy Ryza, Uri Laserson, Sean Owen

In 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.

You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—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 these patterns useful for working on your own data applications.

Patterns include:

Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder

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

278 pages, Kindle Edition

First published April 2, 2015

Reviews

[Taras Fedorov · Rating 5 out of 5]

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

Зрозумілі та конкретні приклади та покрокова інструкція.

[Liam Bui · Rating 5 out of 5]

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 · Rating 5 out of 5]

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 · Rating 5 out of 5]

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 · Rating 4 out of 5]

Good examples.

[Delhi Irc]

DL027817 GG6

[Aboud Akhdir · Rating 5 out of 5]

Great one, very thorough.