Apache Pig vs. IBM Analytics Engine

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.

Incentivized

We are at present utilizing IBM Analytics Engine and it works incredible. Following are the things that I like the most about this product is:- - Simple to Utilize - Reasonable Cost - With only a couple seconds you can ready to fabricate and convey groups - you can without much of a stretch break down information through different applications

Incentivized

• 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."

Incentivized

• We are able to build and deploy clusters within minutes to simplify user experience and increase scalability and reliability.
• We are able to scale and compute on-demand to handle newer workloads like machine learning.
• We really like that we are able to access and administer the application via multiple interfaces.

Incentivized

• May not fit every need and a SQL-like abstraction may be more effective for some tasks (look at Spark-SQL, Hive, or even an actual DBMS)
• All Pig jobs are written in a Domain Specific Language so not a lot of transferable knowledge
• Writing your own User Defined Functions (UDFS) is a nice feature but can be painful to implement in practice

Incentivized

• I would like to see a more robust version of their online help
• The speed of their business support is adequate, but I kind of expect more from such a powerhouse.
• Problems with duration of cluster life

Incentivized

It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.

Incentivized

The documentation is adequate. I'm not sure how large of an external community there is for support.

Incentivized

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.

Incentivized

• I have been using Azure for my previous analysis, I had a difficult time in understanding the Analytics engine rather IBM provided step by step tutorial for setup.
• Also turning off a machine was not an option in Azure for some of the services so I had to pay for the service whether I use it or not

Incentivized

• 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.

Incentivized

• It has saved us quite a bit of time managing our catalog of clusters and keeping things organized.
• Since we had a division we acquired running IBM Cloud, it was easy to get it running and try it out, but we found we prefer our Azure configuration better simply to keep our technology in alignment across corporate functions.
• I definitely see some cost savings by separating out the storage and compute. It helps you start to put an appropriate price tag on certain instances of big data.

Incentivized