Apache Pig vs. SAP Vora

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

I spent more than 1 year with SAP Vora, SAP Datahub and SAP Leonardo with ML, iOt. I believe this product has potential but it is not easy to adopt. SAP has to keep in mind how open-source big data technologies are able to deliver quick results. I know SAP is stabilizing and fighting hard against many open source technologies, but it still has a long way to go there.

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

• Modelling with SAP HANA and Hadoop
• Realtime Analysis using Vora and HANA as a Streaming engine
• Time series Analysis on large chunks of datasets
• Machine learning capabilities on Hadoop tables and spark contexts

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

• Vora 2.0 in on premise scenarios could be improved, as adoption of the cloud is not an easy sell.
• Kubernetes and Docker integration need to be more seamless and quick to understand. If this is simplified, it will be easy to adopt
• Data hub orchestration and integrations could be simplified so that quick adoption within SAP BW, ECC, S4 HANa scenarios is possible.

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

• 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

• Negative impact would be Poc and RFI will need more time to adopt and decision making gets delayed
• Positive impact would be it's a great leap from SAP to adopt a Big data technologies and AI within cloud stream. But selling is going to take time.

Incentivized