{"id":5027,"date":"2020-09-24T19:21:04","date_gmt":"2020-09-24T13:51:04","guid":{"rendered":"https:\/\/www.h2kinfosys.com\/blog\/?p=5027"},"modified":"2020-09-24T19:21:06","modified_gmt":"2020-09-24T13:51:06","slug":"hadoop-pig-tutorial-what-is-architecture-example","status":"publish","type":"post","link":"https:\/\/www.h2kinfosys.com\/blog\/hadoop-pig-tutorial-what-is-architecture-example\/","title":{"rendered":"Hadoop Pig Tutorial: What is, Architecture, Example"},"content":{"rendered":"\n

Introduction to Apache Pig<\/h2>\n\n\n\n

Just like MapReduce, Apache Pig is used to analyze big data sets. It is designed to deliver an abstraction over MapReduce, decreasing the complexity of writing a MapReduce program as a MapReduce program that requires Python or Java Knowledge. Apache Pig helps in performing data manipulation operations very quickly in Hadoop.<\/p>\n\n\n\n

Pig Architecture<\/h2>\n\n\n\n

Pig consists of two components:<\/p>\n\n\n\n

  1. JVM for <\/strong>running PigLatin.<\/li>
  2. Pig Latin,<\/strong> which is a programming language<\/li><\/ol>\n\n\n\n

    A Pig Latin program comprises a sequence of procedures or modifications applied to the input data to create output. These operations describe a data flow which is translated into an executable representation, by Pig execution environment.<\/p>\n\n\n\n

    These transformations provide a level of abstraction that hides a series of MapReduce jobs. This abstraction allows the programmer to focus on data instead of lengthy codes.  <\/p>\n\n\n\n

    PigLatin <\/a>is a moderately strengthened language that uses friendly keywords from data processing, e.g., Join, Group, and Filter.<\/p>\n\n\n\n

    \"\"<\/figure>\n\n\n\n

    Pig has two execution modes:<\/p>\n\n\n\n

    1. Local mode: In local mode, Pig runs on JVM and uses the localhost. This mode is appropriate only for the testing on small datasets using Pig.<\/li>
    2. Map Reduce mode: In MapReduce <\/a>mode, queries written in Pig Latin programming language are rephrased into MapReduce jobs and run on a Hadoop cluster. For running Pig for large datasets, MapReduce mode is used.<\/li><\/ol>\n\n\n\n

      How to Download and Install Pig<\/h2>\n\n\n\n

      Download the pig from the link given below<\/p>\n\n\n\n

      https:\/\/downloads.apache.org\/pig\/pig-0.16.0\/<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Now move the downloaded file to the supper_user<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Now extract the content in the folder using the command given below.<\/p>\n\n\n\n

      sudo tar -xvf pig-0.16.0.tar.gz pig-0.16.0\/<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      Open the bashrc file using the command below.<\/p>\n\n\n\n

      ~\/.bashrc<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      And do the following modifications.<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Now run the following command.<\/p>\n\n\n\n

      . ~\/.bashrc<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Now we need to compile PIG. Run the following commands.<\/p>\n\n\n\n

      cd $PIG_HOME<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Install ANT.<\/p>\n\n\n\n

      sudo apt-get install ant<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
      \"\"<\/figure>\n\n\n\n

      Recompile the PIG<\/p>\n\n\n\n

      sudo ant clean jar-all -Dhadoopversion=23<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      Check if the PIG is installed using the following command<\/p>\n\n\n\n

      pig -help<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

      <\/p>\n","protected":false},"excerpt":{"rendered":"

      Introduction to Apache Pig Just like MapReduce, Apache Pig is used to analyze big data sets. It is designed to deliver an abstraction over MapReduce, decreasing the complexity of writing a MapReduce program as a MapReduce program that requires Python or Java Knowledge. Apache Pig helps in performing data manipulation operations very quickly in Hadoop. […]<\/p>\n","protected":false},"author":1,"featured_media":5065,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[138],"tags":[1409,1408],"class_list":["post-5027","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-hadoop-tutorials","tag-apache-pig","tag-hadoop-pig-tutorial"],"_links":{"self":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/5027","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/comments?post=5027"}],"version-history":[{"count":0,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/posts\/5027\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media\/5065"}],"wp:attachment":[{"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/media?parent=5027"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/categories?post=5027"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.h2kinfosys.com\/blog\/wp-json\/wp\/v2\/tags?post=5027"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}