Schema Registry acts as a service layer for metadata. It stores a versioned history of all the schema of registered data streams and schema change history.
Filtering a Spark Dataset against a collection of data values is a commonly encountered use case for many data analytics scenarios. This article explains four different ways to achieve the same.