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Apache Pig is designed for analysing and interpreting large chunk of data. The first version of Apache Pig was released in 2007 by Yahoo, after which its license was released to Apache Foundation. Think of Apache Pig as a scripting platform running on what is known as Apache Hadoop. For Apache Pig to work, it needs to be compiled using a programming language, and the language used for this purpose is Pig Latin. There is nothing particularly spectacular about Pig Latin – it is just an extension of Ruby and Python, and so if you program in any of the two languages, Pig Latin shouldn’t be much of a problem.
Apache Pig is primarily used for analysing and interpreting large chunk of data, otherwise, would have been difficult for other programming languages. The unique selling point of Apache Pig is its ability to handle huge size of data without breaking up. This has been made possible by the amenable nature of the structure of Apache Pig. Like every other programming platform, Apache pig comes with a compiler. What this compiler does is to produce and execute series of Map-Reduce programs, or transforms as some people prefer to call it. This execution is made possible by the programming language, Pig Latin, which Apache Pig runs on.
The Features of Apache Pig:
Why bother using Apache Pig in the first place? Well, there are lots of reason to do so.
1. Apache Pig is highly extensible and flexible
Apache Pig’s structure has been designed in such a way that users can easily create customizable functions, thus making life easy for programmers.
2. Simple to use
While this scripting platform is capable of handling complex data, it is pretty easy to work with. Understanding the lines of code required to execute a set of data transformation is easy as well.
3. More efficient
For an execution to work efficiently, they have to be properly optimized. Apache Pig automatically takes care of the optimization. With optimization taken care of, a programmer can focus on what really matters: semantics.
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