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Advanced Analytics with Spark: Patterns for Learning from Data at Scale 2nd Edition, ISBN-13: 978-1491972953

Advanced Analytics with Spark: Patterns for Learning from Data at Scale 2nd Edition, ISBN-13: 978-1491972953

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Advanced Analytics with Spark: Patterns for Learning from Data at Scale 2nd Edition, ISBN-13: 978-1491972953

[PDF eBook eTextbook]

  • Publisher: ‎ O’Reilly Media; 2nd edition (July 18, 2017)
  • Language: ‎ English
  • 280 pages
  • ISBN-10: ‎ 9781491972953
  • ISBN-13: ‎ 978-1491972953

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 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

The first chapter will place Spark within the wider context of data science and big data analytics. After that, each chapter will comprise a self-contained analysis using Spark. The second chapter will introduce the basics of data processing in Spark and Scala through a use case in data cleansing. The next few chapters will delve into the meat and potatoes of machine learning with Spark, applying some of the most common algorithms in canonical applications. The remaining chapters are a bit more of a grab bag and apply Spark in slightly more exotic applications—for example, querying Wikipedia through latent semantic relationships in the text or analyzing genomics data.

Since the first edition, Spark has experienced a major version upgrade that instated an entirely new core API and sweeping changes in subcomponents like MLlib and Spark SQL. In the second edition, we’ve made major renovations to the example code and brought the materials up to date with Spark’s new best practices.

Sandy Ryza develops algorithms for public transit at Remix. Prior, he was a senior data scientist at Cloudera and Clover Health. He is an Apache Spark committer, Apache Hadoop PMC member, and founder of the Time Series for Spark project. He holds the Brown University computer science department’s 2012 Twining award for “Most Chill”.

Uri Laserson is an Assistant Professor of Genetics at the Icahn School of Medicine at Mount Sinai, where he develops scalable technology for genomics and immunology using the Hadoop ecosystem.
Sean Owen is Director of Data Science at Cloudera. He is an ApacheSpark committer and PMC member, and was an Apache Mahout committer.

Josh Wills is the Head of Data Engineering at Slack, the founder of the Apache Crunch project, and wrote a tweet about data scientists once.

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