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Learning Hadoop 2

You're reading from   Learning Hadoop 2 Design and implement data processing, lifecycle management, and analytic workflows with the cutting-edge toolbox of Hadoop 2

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Product type Paperback
Published in Feb 2015
Publisher Packt
ISBN-13 9781783285518
Length 382 pages
Edition 1st Edition
Tools
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction 2. Storage FREE CHAPTER 3. Processing – MapReduce and Beyond 4. Real-time Computation with Samza 5. Iterative Computation with Spark 6. Data Analysis with Apache Pig 7. Hadoop and SQL 8. Data Lifecycle Management 9. Making Development Easier 10. Running a Hadoop Cluster 11. Where to Go Next Index

Impala


Hive is not the only product providing SQL-on-Hadoop capability. The second most widely used is likely Impala, announced in late 2012 and released in spring 2013. Though originally developed internally within Cloudera, its source code is periodically pushed to an open source Git repository (https://github.com/cloudera/impala).

Impala was created out of the same perception of Hive's weaknesses that led to the Stinger initiative.

Impala also took some inspiration from Google Dremel (http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/36632.pdf) which was first openly described by a paper published in 2009. Dremel was built at Google to address the gap between the need for very fast queries on very large datasets and the high latency inherent in the existing MapReduce model underpinning Hive at the time. Dremel was a sophisticated approach to this problem that, rather than building mitigations atop MapReduce such as implemented by Hive, instead created a new...

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