[sudsmenon]

Fundamentally, any enterprise today has to deal with OLTP data, OLAP data (transactional plus other sources), Streaming and finally machine learning. Our premise is that you can choose to use 4 different platforms for each one or move to a unified platform. Spark offers streaming, it offers Spark SQL and there are a bunch of machine learning libraries available on Spark. Also, everyone who has anything to do with data has a connector to Spark so it becomes a good data integration platform. The API is uniform across these. So it forms a good substrate for what we are trying to do. As for bugs, it is a platform that is growing rapidly , going through some growing pains, and over a period of time, it will mature. We believe that the core capabilities will become powerful over time (SparkSQL, Streaming etc.) It is a somewhat opinionated choice but one that we think will pan out over time.

[plamb]

Also (correct me if I'm wrong), the stability of SnappyData will depend more on GemFireXD (related to Apache Geode), the in-memory database that has been integrated with Spark to form SnappyData, then it will depend on Spark. GemFire has been in development for over a decade and has a multitude production use cases.