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Hi @Fleid - I noticed you've linked to the older Athena dbt adapter; dbt docs own warehouse setup for Athena links to the newer, community maintained adapter. The newer dbt athena community maintained adapter is the only one that I believe supports modern versions of dbt-core, as well as many of the latest features of the underlying service. I appreciate this discussion may be more about requests by dbtCloud users to have an Athena adapter available in that product. However, continuing to link to the outdated dbt-athena adapter makes it difficult for all new users to find the latest community maintained Athena adapter, within search engine results. If it's appropriate, maybe you could update the link to point to the latest dbt athena adapter |
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Hi @Fleid - (and @dataders - thanks for the prompt on Slack to comment on the discussion) I couldn't see it written on the dbt website, but it seems implicit that verifying a dbt adapter means that it will be hosted under the dbt-labs GitHub organization, and to some extent, supported by dbt-labs adapter team. This seems to correspond to this discussion and your roadmap, in the sense that there is a desire to de-list dbt-spark, and add verification for synapse, trino/starburst etc. I am curious if is permissible that an adapter can be both
This definition is important, because elsewhere in the community, projects look for user support and sometimes to the endorsement of dbt-labs as a guide for whether to support data warehouses:
I understand the risk for dbt-labs in relation to dbtCloud, of including an adapter that goes unmaintained. However, it'd be sad to see Athena held back from awesome products like Lightdash, when it already works so well and delivers a lot of community value through its use with dbt-core. I don't speak for the Athena community adapter maintainers but would happily help out to support the adapter's verification in its current form. |
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Disclaimers:
Introduction, literally
First who am I to talk about this topic? I'm Florian, the product manager for Adapters at dbt Labs. My scope is adapters in core (the default materializations,
dbt-postgres
,dbt-redshift
,dbt-snowflake
,dbt-bigquery
anddbt-spark
) and what we call the cloud adapters, meaning how and which plugin we support in dbt Cloud (adding to the previous listdbt-databricks
and soondbt-trino
).Just to be clear, I am not responsible for all the good stuff happening there - that's primarily your contributions and the contributions of our stellar development team - but rather that if something you want is not being prioritized, I'm the one to blame! Well @jtcohen6 and @dbeatty10 are also to blame. Don't worry about us though: when things go wrong, our solution is to point at one another spiderman-style. It has worked pretty well so far ;)
Roadmap
After that preamble, let's get down to it.
I'm writing this post following a series of asks for us to support
dbt-athena
in dbt Cloud. It is currently one of our most-used community adapters. But at this point in time, it doesn't look like we will be able to support it in Cloud in 2023. There is just too much to do for us, before we can make it happen.What is that? Well here's what we're thinking for 2023. Some of it is started, some planned, some is aspirational:
dbt core
Please see here for the full story
dbt-redshift
2.0 installed, it works with any 2.x version of core) with things like profile rationalization, dependency snapshots, better credential management etc. This is not about achieving that in 2023, but making significant progress towards itdbt Cloud
Not enough time
We have a lot to tackle, as we will also be supporting @jtcohen6 and @MichelleArk initiatives around multi-project deployments. So I'm pretty sure we won't be able to do everything. There will be some hard cuts in that list. To minimize regrets, please be vocal in the comments below in terms of priorities, or you see something missing.
One area where we are considering scaling back our involvement is
dbt-spark
. Now thatdbt-databricks
has joined the party, I have to question the return on investment of "actively" maintainingdbt-spark
. Not only does that repository needs a lot of attention, but Spark is often a challenging platform to work with, and we don't have the bandwidth to build and maintain an expertise of it in-house. All of that for a small user base, highly capable of figuring things out for themselves. I'm not definitive on the topic yet, here also I'd appreciate your feedback and ideas. Be loud if you think it's a bad idea.Thanks for taking the time to read this, I'm looking forward to reading your thoughts below ;)
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