Apache Pig is an abstraction over MapReduce. Data Scientists use Apache Pig. Pig was explicitly developed for non-programmers. Pig uses a language called Pig Latin, which is similar to SQL. Apache Pig performs the task which involves the ad-hoc processing as well as quick prototyping. Pig is complete in that you can do all the required data manipulations in Apache Hadoop with Pig. That's why the name, Pig! It also can be extended with user-defined functions. This is greatly used in iterative processes. Apache Pig Use Cases -Companies Using Apache Pig . Apache Pig is an open-source framework developed by Yahoo used to write and execute Hadoop MapReduce jobs. It typically runs on a client side of clusters of Hadoop. In this article “Apache Pig UDF”, we will learn the whole concept of Apache Pig UDFs. User or developer can combine various types of tasks and create a separate task pipeline. [Related Page: Introduction to HDFS] Why should we use Apache Pig? Apache Pig creates tuples of data. Apache Pig is used: Where we need to process, huge data sets like Web logs, streaming online data, etc. Through Apache Oozie, you can execute two or more jobs in parallel as well. Apache Pig was developed by Yahoo in the year 2006 with the intention to reduce the coding complexity with MapReduce. It is a tool/platform which is used to analyze larger sets of data representing them as data flows. Apache Pig Explain Operator - The explain operator is used to display the logical, physical, and MapReduce execution plans of a relation.One of Pig’s goals is to allow you to think in terms of data flow instead of MapReduce. An integrated part of CDH and supported with Cloudera Enterprise, Pig provides simple batch processing for Apache Hadoop. Apache Pig Tutorial: Where to use Apache Pig? Three parameters need to be followed before setting the environment for Pig Latin: ensure that all Hadoop services are running properly, Pig is completely installed and configured, and all required datasets are uploaded in the HDFS. Pig excels at describing data analysis problems as data flows. Apache Hive is a Hadoop component that is normally deployed by data analysts. Even though Apache Pig can also be deployed for the same purpose, Hive is used more by researchers and programmers. In a MapReduce framework, programs need to be translated into a series of Map and Reduce stages. Apache Pig. In this example, we traverse the data of two columns exists in the given file. The Features of Apache Pig are as follows, 1. However, Pig attains many more advantages in it. Apache Pig Pros and Cons. Pig and Apache Parquet belong to "Big Data Tools" category of the tech stack. Pig originated as a Yahoo Research initiative for creating and executing map-reduce jobs on very large data sets. The software language in use is Pig Latin. Apache Pig was developed to analyze large datasets without using time-consuming and complex Java codes. Pig Latin and Pig Engine are the two main components of the Apache Pig tool. Similar to Pigs, who eat anything, the Pig programming language is designed to work upon any kind of data. Moreover, we will also learn its introduction. Both Apache Pig and Apache Hive is a powerful tool for data analysis and ETL. 2. However, this is not a programming model which data analysts are familiar with. This package contains implementations of Pig specific data types as well as support functions for reading, writing, and using all Pig data types. Apache HCatalog Apache HCatalog is a storage and table management tool for sorting data from different data processing tools. Apache Parquet with 918 GitHub stars and 805 forks on GitHub appears to be more popular than Pig with 580 GitHub stars and 447 GitHub forks. • It is a tool/platform which is used to analyze larger sets of data representing them as data flows. Apache pig has a rich collection set of operators in order to perform operations like join, filer, and sort. As we all know, we use Apache Pig to analyze large sets of data, as well as to represent them as data flows. The result of Pig always stored in the HDFS. Generally, the Apache Pig gives an abstraction to reduce the complexity of developing MapReduce Programming for the developers. A user needs to select a tool based on data types and expected output. It is very similar to SQL. Pig is a scripting platform that runs on Hadoop clusters, designed to process and analyze large datasets. These tasks can belong to any of the Hadoop components like Pig, Sqoop, MapReduce or Hive etc. a local execution enviornment in a single JVM (used when dataset is small in size)and distributed execution enviornment in a Hadoop Cluster. org.apache.pig.impl The logical operators that represent a pig script and tools for manipulating those operators. • Apache Pig is an abstraction over MapReduce. What is Pig in Hadoop? Apache pig has a rich set of datasets for performing different data operations like join, filter, sort, load, group, etc. It is designed to provide an abstraction over MapReduce, reducing the complexities of writing a MapReduce program. Apache Pig UDF (Pig User Defined Functions) There is an extensive support for User Defined Functions (UDF’s) in Apache Pig. Rich set of operators . 1. A high-level platform for creating programs that run on Hadoop, Apache Pig can execute its Hadoop jobs in MapReduce, Apache Tez, or Apache Spark. Apache Pig and Apache Hive are mostly used in the production environment. And data stored in Hive is in table records, just like a relational database. So they are not so easy to use together. To process more time sensitive for the data load. Pig was a result of development effort at Yahoo! Apache Pig is a platform, used to analyze large data sets representing them as data flows. • Pig is generally used with Hadoop; we can perform all the data manipulation operations in Hadoop using Apache Pig. Let’s study about Features Application Apache Pig to make use of it in the projects. people in the IT industry are using it for Big Data Log Analysis, If you know Python, R, Scala Programming Language then Apache Pig will be very very easy to learn.. Features of Apache Pig. Pig and Apache Parquet are both open source tools. So the three have different ways of storing data. The Pig Scripts are give in to the Pig Engine that convert the Pig Latin scripts into MapReduce jobs. 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