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Hadoop Real-World Solutions Cookbook- Second Edition

You're reading from   Hadoop Real-World Solutions Cookbook- Second Edition Over 90 hands-on recipes to help you learn and master the intricacies of Apache Hadoop 2.X, YARN, Hive, Pig, Oozie, Flume, Sqoop, Apache Spark, and Mahout

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Product type Paperback
Published in Mar 2016
Publisher
ISBN-13 9781784395506
Length 290 pages
Edition 2nd Edition
Tools
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Author (1):
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Tanmay Deshpande Tanmay Deshpande
Author Profile Icon Tanmay Deshpande
Tanmay Deshpande
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Table of Contents (12) Chapters Close

Preface 1. Getting Started with Hadoop 2.X FREE CHAPTER 2. Exploring HDFS 3. Mastering Map Reduce Programs 4. Data Analysis Using Hive, Pig, and Hbase 5. Advanced Data Analysis Using Hive 6. Data Import/Export Using Sqoop and Flume 7. Automation of Hadoop Tasks Using Oozie 8. Machine Learning and Predictive Analytics Using Mahout and R 9. Integration with Apache Spark 10. Hadoop Use Cases Index

Processing Hive data in the Avro format


Avro is an evolvable schema-driven binary data format. It is hosted and maintained by the Apache Software Foundation (http://avro.apache.org/). It provides a rich data structure to store compact, fast binary data, and it relies on schemas. Avro files store data and schemas together; this helps faster reading of data as the files do not need to look for schema anywhere else. It can also be used in Remote Procedure Calls (RPC). There, the schema is transferred at the time of handshake between a client and server. In this recipe, we will take a look at how to process Avro files in Hive.

Getting ready

To perform this recipe, you should have a running Hadoop cluster as well as the latest version of Hive installed on it. Here, I am using Hive 1.2.1. Hive has built-in support for the Avro file format, so we don't need to import any third-party JARs.

How to do it...

Using Avro SerDe, we can either read data that is already in the Avro format or write new data...

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