I've come across this problem a few times, and I am a recently upskilled AI engineer with no prior data engineering experience. My most recent example was CRM related - I wanted to see my entire sales funnel and I had a few different data sources and APIs like linkedin (I'm in EU so I get access to crazy data access via Member portability APIs), app events, gmail/calendar, apollo for enrichment etc... that I wanted to combine, track, and store over time. I have so much data from different sources and getting these together is a massive pain (I also didn't need a complex CRM for this - just wanted to make it queryable via Claude and create some visuals with Streamlit.)

The way I think about it is: these are just APIs and data sources. I fetch the data, and then I want to pipeline it and structure it in my own little database, in a way that makes sense to me. Then I can enrich it, expand it, change the schema, query it, build dashboards, apps, whatever (freedom...).

The problem is that every time I started doing this I'd end up yak shaving for days. Which DB? Where do the pipeline files live? How do I productionise cron jobs? I want to make it operational via API and MCP (agentic-friendly semantics built in). Essentially, my vision is to have a dedicated app for each of my departments, but doing this every time is a massive pain, even with a boilerplate.

So... me and my bro built Loony.dev, which is essentially a CLI optimised for your Claude agent, and it does all of this out of the box. Just bring your data sources - deploy your env variables to Loony, and it can pull the data, build the pipelines, set the update schedules and build agentic-ready views with MCP / REST-API out of the box.

Would love to know if this resonates with anybody else? Feels like I was running into this issue every other week and I just gave up because it felt like I was building another tiny startup each time.