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Our framework (and the CLI interface) is pretty similar to be honest, except it’s in JS. We also spent quite a bit of time focusing on performance.

The main difference is that we provide an all-in-one web platform for teams to develop, test and schedule their SQL pipelines. With one web interface and in 5 min, you can write a query, test it, add it to a schedule, push the changes to GitHub (or submit a PR) and monitor your jobs.

We make it easy to enforce software engineering best practices like version control and isolated deployments. For example, our version control works similarly to Looker. Analysts can work simultaneously from different branches while not having to use different tools nor use the command line.

What’s the rationale behind using JS instead of Python/Jinja? Any insights on that?

Great question :)

1. Speed. We wanted compilation to really, really fast. 2. In all honesty we just weren't big fans of Jinja, having used it for quite a while. JS templating is OK out the box, and we are considering React like syntax in the future. 3. Love it or hate it, NPM packages are pretty easy to work with and we are working on a number of packages at the moment. 4. When you start to look at things like UDFs and Cloud functions which enables some really cool use cases, JS seems to be prevailing (in Snowflake and BigQuery at least).

I will admit though that we do usually get a bit of a shocked reply when people hear it's not Python!

I see your point but I think that data analysts are usually familiar with Python, not JS. The UI is cool and intuitive, it looks like you copied most of the concepts except Jinja from DBT. That's perfectly OK though, let's see where it goes!

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