Note: Apache Pig is an abstract layer or high-level language on top of HDFS as every statement of the pig is internally getting converted into MR.
[ Related Page:- Introduction to HDFS (Distributed File System) - Hadoop ]
1. In MapReduce, for processing data we have to write the driver code, Mapper code, and Reduces code (if required) irrespective of business logic that we are applying Whereas, in Apache pig, we can archive some functionality by making use of scripting language with less number of lines of coding.
2. MapReduce is expecting Java programming language skills whereas in apache pig even a nonjava programming member can write the code using simple scripting.
3. 200 lines of MR code are equal to 10 lines of a pig code.
4. In Map-reduce, we have to follow scripting process something like a compilation of MR code, Executing code, packaging code, and deploy in the cluster whereas, in apache pig, it is very easy to run the code without involving many steps
Interested To Learn Map Reduce Certification Training? Enroll now for FREE Demo On Map Reduce Training!
|Big Data On AWS||Informatica Big Data Integration|
|Bigdata Greenplum DBA||Informatica Big Data Edition|
|Hadoop Testing||Apache Mahout|
Stay updated with our newsletter, packed with Tutorials, Interview Questions, How-to's, Tips & Tricks, Latest Trends & Updates, and more ➤ Straight to your inbox!
|Hadoop Training||Jul 02 to Jul 17|
|Hadoop Training||Jul 05 to Jul 20|
|Hadoop Training||Jul 09 to Jul 24|
|Hadoop Training||Jul 12 to Jul 27|
Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
Copyright © 2013 - 2022 MindMajix Technologies