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Mastering Hadoop

You're reading from   Mastering Hadoop Go beyond the basics and master the next generation of Hadoop data processing platforms

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Product type Paperback
Published in Dec 2014
Publisher
ISBN-13 9781783983643
Length 374 pages
Edition 1st Edition
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Author (1):
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Sandeep Karanth Sandeep Karanth
Author Profile Icon Sandeep Karanth
Sandeep Karanth
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Table of Contents (15) Chapters Close

Preface 1. Hadoop 2.X FREE CHAPTER 2. Advanced MapReduce 3. Advanced Pig 4. Advanced Hive 5. Serialization and Hadoop I/O 6. YARN – Bringing Other Paradigms to Hadoop 7. Storm on YARN – Low Latency Processing in Hadoop 8. Hadoop on the Cloud 9. HDFS Replacements 10. HDFS Federation 11. Hadoop Security 12. Analytics Using Hadoop A. Hadoop for Microsoft Windows Index

Pig versus SQL

SQL is a very popular query and data processing language. Any high-level language for data processing deserves comparison with SQL. In this section, we will compare Pig Latin with SQL. The comparison is as follows:

  • Pig Latin is primarily a procedural language. SQL, on the other hand, is declarative in nature. The data pipeline in SQL is not expressed as the data transformations happen. However, in Pig Latin, each step of the data transformation in the pipeline is specified in order. It is possible to mimic this behavior in SQL with the use of intermediate temporary tables, but creating, managing, and cleaning up these intermediate tables can be cumbersome and error-prone. Though Pig Latin scripts are specified procedurally, the statements are executed lazily, that is, they are not executed until the value is absolutely required.
  • Developers writing data flows in a declarative language such as SQL overly depend on the query optimizer to choose the right implementation for the...
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