Best Apache Pig and Hadoop Solutions
Apache Pig is an open-source technology and a high-level data analysis language that is used to examine large data sets. It is generally used with Hadoop and was mainly developed to simplify the job of writing mapper and reducer programs in Java.
Pig programming language is crafted to handle any kind of data. Previously, Hadoop developers had to write complex java codes to analyse large data sets, but with Apache pig this process has become easy. It consists of a high level programming language called Pig Latin and a runtime environment where these Pig Latin scripts are executed and broken down into mapreduce tasks and data has been extrapolated.
The Apache Pig Advantage
As mentioned earlier, Apache Pig is a data flow language and was designed mainly to help Hadoop developers analyse large pools of data and with its textual language Pig Latin, the following are the advantages:
- Less time for development
- Lengthy Codes are reduced by using multi-query approach
- Flexible and users can create their own functions
- Highly optimized and allows users to focus on the logic
- Enjoys everything that Hadoop offers such as parallelization, fault tolerance etc
So how does Apache Pig perform this tedious task of data analysis?
Programmers have to first feed in the scripts using the Pig Latin language and then the Pig complier breaks these scrips into smaller mapreduce jobs simultaneously and then they are stored as HDFS (Hadoop Distributed File System). In times when the data set is huge and logical operations are difficult to run in the Pig Latin script, it can be extended with user defined functions (UDF) which users can write in Java, Python, Ruby.
How does Apache Pig work?
The most easiest way of understanding how Apache Pig works is to think of the ETL process, Extract – Transform – Load.
- In the first step, Pig extracts data from the different source files using UDF. This is the input.
- In the second stage, all the data that has been extracted undergoes initial and further processing with multiple iterations
- In the final stage, Pig stores this data in the Hadoop Distributed File Systems.
Thus all these tasks are a series of MapReduce jobs that runs on a hadoop cluster and Pig continues to optimize them.
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