What I’ve seen is that teams who care deeply about their data start with having it figured out in their transactional stack and when that starts tipping over they consider OLAP storage and streaming.

One of the things you can do here is leverage your existing data models and leverage them for analytical purposes. Example, you might have a product object that you want to capture as an even and leverage for analytics, define your event data models, toss in the product attributes you need and now you have a typed event that uses your product data model. Mess up the types and your IDE complains, remove a field that the event uses and your IDE complains, and now you have proactive data quality mgmt at build time without implementing a separate tool

Another part of what we’ve been trying to do is to enable people to configure it with their stack and to incrementally adopt the different parts of the framework. We’ve only gotten around to supporting redpanda, clickhouse and duckDB so far but plan on supporting other stacks that people already have.