Likelihood to Recommend 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.
Read full review My recommendation obviously would depend on the application. But I think given the right requirements, IBM DB2 Big SQL is definitely a contender for a database platform. Especially when disparate data and multiple data stores are involved. I like the fact I can use the product to federate my data and make it look like it's all in one place. The engine is high performance and if you desire to use Hadoop, this could be your platform.
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Pros Its performance, ease of use, and simplicity in learning and deployment. Using this tool, we can quickly analyze large amounts of data. It's adequate for map-reducing large datasets and fully abstracted MapReduce. Read full review data storage data manipulation data definitions data reliability Read full review Cons UDFS Python errors are not interpretable. Developer struggles for a very very long time if he/she gets these errors. Being in early stage, it still has a small community for help in related matters. It needs a lot of improvements yet. Only recently they added datetime module for time series, which is a very basic requirement. Read full review Cloud readiness. Ease of implementation. Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Usability It is quick, fast and easy to implement Apache Pig which makes is quite popular to be used.
Read full review IBM DB2 is a solid service but hasn't seen much innovation over the past decade. It gets the job done and supports our IT operations across digital so it is fair.
Read full review Support Rating The documentation is adequate. I'm not sure how large of an external community there is for support.
Read full review IBM did a good job of supporting us during our evaluation and proof of concept. They were able to provide all necessary guidance, answer questions, help us architect it, etc. We were pleased with the support provided by the vendor. I will caveat and say this support was all before the sale, however, we have a ton of IBM products and they provide the same high level of support for all of them. I didn't see this being any different. I give IBM support two thumbs up!
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Alternatives Considered Apache Pig might help to start things faster at first and it was one of the best tool years back but it lacks important features that are needed in the data engineering world right now. Pig also has a steeper learning curve since it uses a proprietary language compared to Spark which can be coded with Python, Java.
Read full review MS SQL Server was ruled out given we didn't feel we could collapse environments. We thought of MS-SQL as more of a one for one replacement for Sybase ASE, i.e., server for server.
SAP HANA was evaluated and given a big thumbs up but was rejected because the SQL would have to be rewritten at the time (now they have an accelerator so you don't have to). Also, there was a very low adoption rate within the enterprise. IBM DB2 Big SQL was not selected even though technically it achieved high scores, because we could not find readily available talent and low adoption rate within the enterprise (basically no adoption at the time). We ended up selecting Exadata because of the high adoption rate within the enterprise even though technically HANA and Big SQL were superior in our evaluations.
Gene Baker Vice President, Chief Architect, Development Manager and Software Engineer
Read full review Return on Investment Higher learning curve than other similar technologies so on-boarding new engineers or change ownership of Apache Pig code tends to be a bit of a headache Once the language is learned and understood it can be relatively straightforward to write simple Pig scripts so development can go relatively quickly with a skilled team As distributed technologies grow and improve, overall Apache Pig feels left in the dust and is more legacy code to support than something to actively develop with. Read full review better data visibility solid reliability for mission critical data Read full review ScreenShots