Best Apache Pig and Hadoop Solutions
Apache Pig is an open-source technology and a high-level data analysis language used to examine large datasets. It is generally used with Hadoop and was mainly developed to simplify the work of writing mapping and reduction programs in Java.
The Pig programming language is designed to manage all types of data. Previously, Hadoop developers had to write complex java code to analyze large datasets, but with Apache Pig, this process has become easy. It consists of a high-level programming language called Pig Latin and an execution environment where these Pig Latin scripts are executed and broken down into MapReduce tasks and the data has been extrapolated.
The Apache Pig Advantage
As mentioned earlier, Apache Pig is a data flow language and was designed primarily to help Hadoop developers analyze large data pools and with its Pig Latin text language, the following benefits are:
- Less time for development
- Lengthy Codes are reduced by using the multi-query approach
- Flexible and users can create their own functions
- Highly optimized and allows users to focus on the logic
- Take advantage of everything Hadoop offers such as parallelization, fault tolerance, etc.
So how does Apache Pig perform this tedious task of data analysis?
Programmers must first feed the scripts using the Latin language Pig, then the Pig complier breaks these scripts into small simultaneous reduction jobs, then they are stored in HDFS (Hadoop Distributed File System). In times when the dataset is huge and logical operations are difficult to execute in the Pig Latin script, it can be extended with user-defined functions (UDF) that users can write in Java, Python, Ruby.
How does Apache Pig work?
The easiest way to understand how Apache Pig works is to think of the ETL process, Extract – Transform – Load.
- In the first step, the Pig extracts the data from the different source files using UDF. This is the entrance.
- In the second step, all the extracted data undergo initial and subsequent processing with several iterations
- In the final step, Pig stores this data in Hadoop distributed file systems.
Thus, all these tasks are a series of MapReduce jobs that are executed on a Hadoop cluster and Pig continues to optimize them.
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