The README would benefit from a comparison to other tools.
I’m not (necessarily) motivated to switch tooling because of the language it is written in. I’m motivated to switch tooling if it has better ergonomics, performance, or features.
good point, thanks. I'll definitely add some more details about the comparison between different tools.
I agree with you 100% on the language part, I think it is an interesting detail for a data tool to be built in Go, but we have a lot more than that, a couple of things we do there is:
- everything is local-first: native Python support, local VS Code extension, isolated local environments, etc
- very quick iteration speed: rendered queries, backfills, all running locally
- support for data ingestion, transformation, and quality, without leaving the framework, while also having the ability to extend it with Python
these are some of the improvements we focused on bringing into the workflows, I hope this explains our thinking a bit more.
great question! Meltano, if I am not wrong, only does data ingestion (Extract & Load), whereas we go further into the pipeline such as transformation with SQL and Python, ML pipelines, data quality, and more.
I guess a more comparable alternative would be Meltano + dbt + Great Expectations + Airflow (for Python stuff), whereas Bruin does all of them at once. In that sense, Bruin's alternative would be a stack rather than a single product.
ellisv|1 year ago
I’m not (necessarily) motivated to switch tooling because of the language it is written in. I’m motivated to switch tooling if it has better ergonomics, performance, or features.
karakanb|1 year ago
I agree with you 100% on the language part, I think it is an interesting detail for a data tool to be built in Go, but we have a lot more than that, a couple of things we do there is:
- everything is local-first: native Python support, local VS Code extension, isolated local environments, etc
- very quick iteration speed: rendered queries, backfills, all running locally
- support for data ingestion, transformation, and quality, without leaving the framework, while also having the ability to extend it with Python
these are some of the improvements we focused on bringing into the workflows, I hope this explains our thinking a bit more.
karakanb|1 year ago
I guess a more comparable alternative would be Meltano + dbt + Great Expectations + Airflow (for Python stuff), whereas Bruin does all of them at once. In that sense, Bruin's alternative would be a stack rather than a single product.
Does that make sense?
tinodb|1 year ago