Comment by trickyager 5 days ago
Congrats to the SDF team for their exit.
Alas, dbt Labs has developed a reputation for rug pulling functionality from dbt Core and gating most of their differentiating features behind dbt Cloud. I cannot see this type of consolidation being in the best interest of the dbt community.
I've been on the lookout for a lighter, faster version of dbt and I was hoping sdf might be it.
For our (https://www.definite.app/) use case, I'd love to have something that compiles client-side, but in general dbt just feels like a lot of work to set up for what most of our customers actually need (simple transform to create tables and views).
A lot of work to set up?
I'm quite surprised to hear that.
It's literally pip install, a single file for your DB config and that's it. 30-40 seconds.
I'm in no way affiliated with dbt but have worked with the tool since 2018.
Lighter, faster, sure, but hard to set up?
I'm not sure where you'd want to cut corners on setup.
Not to mention the sudden pricing change at the end of 2022 that doubled costs for most cloud customers.
Have they introduced any interesting features outside of core? Most of it seems like fluff, or there are better alternatives.
Yeah my experience is much closer to that, I generally point my clients to core over cloud even if they're indifferent to the cost. (Sorry dbt guys, love your product, but somebody read the strategy memo backwards and you've got lock-out not lock-in. Replacing my IDE, ci/cd, or orchestration are "dealbreakers", not "features")
A brief list of features withheld or removed from Core:
- The dbt docs functionality is no longer maintained in favor of dbt Explorer in dbt cloud. A natural consequence is that larger dbt Core projects simply cannot leverage local docs due to performance defects.
- Multi-project support was widely discussed in the core repo w/ tooling contributions from the community, but that was locked behind enterprise-tier dbt cloud accounts
- Metricflow was a full OSS application that used to work in tandem with dbt Core. Post-acquisition, the original code was re-licensed and the functionality added to Cloud only (and you have to pay per semantic layer query now).
Checking their own post, https://www.getdbt.com/product/dbt-core-vs-dbt-cloud, stuff such as the semantic layer and column-level lineage are Cloud exclusive
dbt Labs is a Series D company with hundreds of millions in funding and a 4.2 billion USD valuation at their last round.
Their CEO and founder spoke of an IPO in 2022.
Let's not pretend they are still remotely close to their humble beginnings or were able to get this far without credibly demonstrating they have a plan for how to make enterprises bleed through their nose for their product.
That's the future.
On the flipside, building a dbt adjacent product enhancing or complementing capabilities is basically a sure way of how to get bought.