[haohui]

Our experience is that AthenaX actually lowers our support costs:

(1) There are significant loads on consultations when users had to implement their own jobs in Java / Scala and run them in production. Sometimes it turned in to co-development as the users lack the expertise of the streaming analytics frameworks.

(2) We consciously encourage our users to write good SQLs via: (a) enforcing schemas on all analytical Kafka topics. (b) setting up a team dedicated to help them using SQL in big data systems (i.e., Hive, Presto, AthenaX, etc.)

For UDF we provide general guidances and ask our users to oncall for the jobs that use UDFs. The support costs are definitely not zero but it is still much better to teach users to write a Samza / Flink / Storm job from scratch.