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Spark Cookbook

You're reading from   Spark Cookbook With over 60 recipes on Spark, covering Spark Core, Spark SQL, Spark Streaming, MLlib, and GraphX libraries this is the perfect Spark book to always have by your side

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Product type Paperback
Published in Jul 2015
Publisher
ISBN-13 9781783987061
Length 226 pages
Edition 1st Edition
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Author (1):
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Rishi Yadav Rishi Yadav
Author Profile Icon Rishi Yadav
Rishi Yadav
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Table of Contents (14) Chapters Close

Preface 1. Getting Started with Apache Spark 2. Developing Applications with Spark FREE CHAPTER 3. External Data Sources 4. Spark SQL 5. Spark Streaming 6. Getting Started with Machine Learning Using MLlib 7. Supervised Learning with MLlib – Regression 8. Supervised Learning with MLlib – Classification 9. Unsupervised Learning with MLlib 10. Recommender Systems 11. Graph Processing Using GraphX 12. Optimizations and Performance Tuning Index

Introduction


Spark can process data from various data sources such as HDFS, Cassandra, HBase, and relational databases, including HDFS. Big data frameworks (unlike relational database systems) do not enforce schema while writing. HDFS is a perfect example where any arbitrary file is welcome during the write phase. Reading data is a different story, however. You need to give some structure to even completely unstructured data to make sense out of it. With this structured data, SQL comes very handy when it comes to analysis.

Spark SQL is a relatively new component in Spark ecosystem, introduced in Spark 1.0 for the first time. It incorporates a project named Shark, which was an attempt to make Hive run on Spark.

Hive is essentially a relational abstraction, which converts SQL queries to MapReduce jobs.

Shark replaced the MapReduce part with Spark while retaining most of the code base.

Initially, it worked fine, but very soon, Spark developers hit roadblocks and could not optimize it any further...

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