Where to find advanced analytics with spark?

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.

Youll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniquesincluding classification, clustering, collaborative filtering, and anomaly detectionto 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, youll find the books patterns useful for working on your own data applications.

With this book, you will:

• Familiarize yourself with the Spark programming model
• Become comfortable within the Spark ecosystem
• Learn general approaches in data science
• Examine complete implementations that analyze large public data sets
• Discover which machine learning tools make sense for particular problems
• Acquire code that can be adapted to many uses

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.

Youll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniquesclassification, collaborative filtering, and anomaly detection among othersto 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, youll 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

Apache Spark is amazing when everything clicks. But if you havent seen the performance improvements you expected, or still dont feel confident enough to use Spark in production, this practical book is for you. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources.

Ideal for software engineers, data engineers, developers, and system administrators working with large-scale data applications, this book describes techniques that can reduce data infrastructure costs and developer hours. Not only will you gain a more comprehensive understanding of Spark, youll also learn how to make it sing.

With this book, youll explore:

• How Spark SQLs new interfaces improve performance over SQLs RDD data structure
• The choice between data joins in Core Spark and Spark SQL
• Techniques for getting the most out of standard RDD transformations
• How to work around performance issues in Sparks key/value pair paradigm
• Writing high-performance Spark code without Scala or the JVM
• How to test for functionality and performance when applying suggested improvements
• Using Spark MLlib and Spark ML machine learning libraries
• Sparks Streaming components and external community packages

Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates.

Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. Youll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.

• Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell
• Leverage Sparks powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib
• Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm
• Learn how to deploy interactive, batch, and streaming applications
• Connect to data sources including HDFS, Hive, JSON, and S3
• Master advanced topics like data partitioning and shared variables