Apache Pig vs. Starburst Enterprise

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

If you run a SQL query, Starburst Presto can help you track efficiently the status of SQL query. It can also track how many resources have been allocated for execution and what is the optimal way to run the query without hindrance. It also provides good information on worker nodes and how parallel threads are running together.

Incentivized

• 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.

Incentivized

• Query tracking.
• Resource allocation.
• Parallelizing query execution.

Incentivized

• 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.

Incentivized

• Pricing
• Platform can be made more intuitive.
• Sometimes the platform hangs while aborting the query.

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

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.

Incentivized

Most of our systems were compatible with Starburst Presto. The dashboard which they provide was fairly intuitive and easy to use. The learning curve wasn't that much. Also, the parallel processing part was an additional feature that we didn't find in many competitive products. The pricing was a little higher but it was worth the trade-off.

Incentivized

• 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.

Incentivized

• Neutral impact.
• ROI on saving time for query execution.
• Parallel processing saves time too.

Incentivized