{"id":57409,"date":"2018-02-02T13:54:16","date_gmt":"2018-02-02T08:24:16","guid":{"rendered":"https:\/\/www.whizlabs.com\/?p=57409"},"modified":"2018-02-02T13:54:16","modified_gmt":"2018-02-02T08:24:16","slug":"pig-progression-with-hadoop-versions","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/pig-progression-with-hadoop-versions\/","title":{"rendered":"Apache Pig Progression with Hadoop\u2019s Changing Versions"},"content":{"rendered":"

Hadoop has scaled up in many ways to open up wings of all levels of technical people. Of course, Java programmers have the edge over others when it comes to Hadoop development. However, if you are new or unknown to high-level languages like Java or Jython, no worries! Apache Pig is there to do all kinds of data manipulations whether structured or unstructured data. Certainly, it makes \u201cPig Hadoop\u201d the interrelated terms in the Hadoop family. <\/span>The purpose of<\/span> <\/span>Apache Pig is to create the MapReduce jobs on large data-sets instead of executing them by writing complex Java codes.<\/span><\/p>\n

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However, Hadoop has evolved in years. Not to mention, this has happened due to increased user demands in the field of data analysis with the massive amount of data. Consequently, every Hadoop components have marketed with some new features on new releases, and so is the Apache Pig. We will have a closer look at those change areas of Apache Pig major releases in this article. <\/span><\/p>\n

Apache Pig in Few Words<\/span><\/b><\/h2>\n

Apache Pig is a high-level scripting language which makes a Hadoop developer’s life easy in making complex data transformations. It is a SQL like procedural language, more widely known as Pig Latin. It is very compatible with users who know other scripting languages. However, to handle real business problems, the compatibility of Pig Big data with its User Defined Functions (UDF) feature works wonderfully. It efficiently invokes code in other languages like JRuby, Java, etc. Also, developers can embed Pig scripts in other languages.<\/span><\/p>\n

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Why is Apache Pig Useful When Hadoop has its MapReduce?<\/span><\/b><\/h4>\n

Both MapReduce and Pig do data processing. However, the first one deals with a low level of abstraction during data set processing. On the other hand, the Pig processes large data sets with the higher level of abstraction<\/strong>. Moreover, you will get a series of MapReduce jobs out of Pig transformations. Along with it, framework wise there are some more differences between MapReduce Processing and Pig processing.<\/span><\/p>\n

With Pig Latin, you can perform almost all the standard data-processing operations, such as group by, join, filter, union, order by, etc. However, you can perform only operations like group by using MapReduce. The other operations like order by, filter, projection and join are not provided in MapReduce. As a result, the user needs to write a custom program for it.<\/span><\/p>\n

Apache Pig Hadoop Versions Over the Years<\/span><\/b><\/h2>\n

Since the time of incubation till today, Apache Pig has evolved with twenty-four releases with different versions of Hadoop. <\/span><\/p>\n

Apache Pig Evolution in Hadoop 1.0 Series<\/span><\/b><\/h4>\n

The first release of Apache Pig came with Hadoop 0.18, and it was in its incubation. However, it was not a stable release from Hadoop perspective. The next releases of Apache Pig which was a maintenance release played as the first version as Hadoop subproject. We see following necessary changes in Pig functionality and performance in the subsequent few releases from Pig 0.1.1 to 0.10.<\/span><\/p>\n

Features included<\/span><\/b><\/p>\n