This is a huge concern for me at my current organization. Dev has decided to put all data into mongoDB. Yet all decisions are based on that data and the tools we have do not allow for seamless flow (ETL) from mongoDB. That data is important for deriving decisions that affect revenue and costs. Where are solutions for the data analysts and scientists? Frankly I'm pretty sick of hearing it can just be automated.

In my mind there has to be a decent "business intelligence stack". I'm not sure I'm coining that because I didn't get good search results from that phrase. Believe me I've been trying to find solutions. I believe there is big opportunity in building out this sort of stack that bridges data management and data analysis. Sure you can call IBM, Microsoft, Dell, HP but be prepared for big costs and huge software bloat. I would like simplified solutions and options that can fit with most industry standard tools.

I'm also willing to work with anyone on this as well.

- hybrid transactional and analytical processing (HTAP), coined by Gartner, - hybrid operational and analytical workloads (HOAP), by 451 Research - Translytical, by Forrester

If that's the solution you want to explore, TiDB (https://github.com/pingcap/tidb), the open source distributed scalable HTAP database, might be able to help you. ETL is no longer necessary with TiDB’s hybrid OLTP/OLAP architecture.

Here is a use case about how it helps the largest B2C fresh produce online marketplace in China to acquire real-time intelligence:

https://www.datanami.com/2018/02/22/hybrid-database-capturin...

Here is a tutorial about how you can try TiDB/TiSpark on your own laptop using Docker Compose: https://www.pingcap.com/blog/how_to_spin_up_an_htap_database...

Disclaimer: I work for TiDB.