Apache Pig is best suited for ETL-based data processes. It is good in performance in handling and analyzing a large amount of data. it gives faster results than any other similar tool. It is easy to implement and any user with some initial training or some prior SQL knowledge can work on it. Apache Pig is proud to have a large community base globally.
What I like most is the IN Memory capability it is doing, as we all know RAM is faster than disk. The capability to connect different databases. Beginners should also take note of the Data Disk Management because anytime it could go wrong, you should have experience in dealing with this kind of event.
Iterative Development - you can write aliases/variables, which are not immediately executed and these are stored in a DAG, which is only evaluated upon dumping or storing another alias.
Fast execution - Works with MapReduce, Tez, or Spark execution frameworks to provide fast run times at large scales.
Local and remote interoperability - Scripts that depend on testing a small dataset locally before moving to the full thing can simply be done with "pig -x local."
It takes me less time to write a Pig script than get a Spark program running for batch ETL workloads. Compared to Spark, Pig has a steeper learning curve because it employs a proprietary programming language. In one script and one fine, it can handle both Map Reduce and Hadoop. It has a large amount of documentation available to make learning more convenient.
The understandable and complete tables and graphs, the cleaning methods and the way of encrypting the data are quite feasible, which does not help to prepare our data, it helps that the data that is thrown as results is separated from each other, the process prior to structuring requires high-level advice and is somewhat time-consuming, there is a risk that they overwrite the data themselves by accident at a later time
Return on Investments are significant considering what it can do with traditional analysis techniques. But, other alternatives like Apache Spark, Hive being more efficient, it is hard to stick to Apache Pig.
It can handle large datasets pretty easily compared to SQL. But, again, alternatives are more efficient.
While working on unstructured, decentralized dataset, Pig is highly beneficial, as it is not a complete deviation from SQL, but it does not take you in complexity MapReduce as well.
