Thoughts on possible implications for users in foreseeable future? We built a lot using dbt and can't really think of going back or switching to alternatives
Full disclosure: I am a PM at Fivetran who is very excited about this.
We are fully committed to open source dbt and don't want to build a 'walled garden'. Interoperability is one of the key value propositions of both Fivetran and dbt. While I'm biased, I think the main implications for users is that their favorite tooling will be with one vendor who cares about what makes them great.
> Data startups Fivetran and dbt Labs will merge in an all-stock deal, creating a combined data infrastructure company with nearly $600 million in annual revenue, the two companies told Reuters.
The "modern data stack" market excluding of data warehouse / data lake is pretty small. Fivetran is biggest one and still under $500M in revenue, so they're acquiring other parts and starting to offer their own datalake (managed Iceberg).
Snowflake started offering fivetran-like connectors a couple years ago and I expect they'll double down there. Same with Databricks. Microsoft has Fabric now, but the reviews have been terrible (my experience included).
I think they'll each ultimately have a full data stack.
If you don't want to wait, we built a modern-data-stack-in-a-box at Definite (https://www.definite.app/).
Interesting. It makes sense with Fivetran and dbt being so complimentary, ingestion (Fivetran) vs transformation (DBT).
But I still feel like I'm missing, in DBT, capabilities for DB DDL/DML deployment (which I've done from Liquibase in DBT) for a fully CICD modern data stack. Preconditions, post-conditions, only-deploy-changed-code capabilities... Am I missing something in DBT?
Fivetran acquired Census (reverse-etl) & Tobiko (dbt alternative).
I wonder who's next to really consolidate their platform play and compete with the old legacy MDM provider like Informatica. Data Observability or Catalog like Monte Carlo and Atlan. The whole Modern Data Stack has either died, acquired or merged by now. Wonder what's missing for Fivetran to IPO too.
I also wonder what this merge means for Airbyte who raised 150m at 1.5b in 2023.
I think consolidation in the space has been coming for quite some time now and this merger only confirms what us, along with many others, have been saying: the data tooling is in a miserable state and we had to glue together a bunch of different tools that don't work with each other.
At this point, I think it is quite obvious that Fivetran is going for Snowflake/Databricks's market share. They own the ingestion for many companies already, and they will offer a managed data lake product in order to compete with the data giants. By owning the means of bringing the data in (Fivetran) as well as the transformation layer (dbt/sqlmesh) they will aim to get ahead of Snowflakes of the world.
I think it'll be a win for the data community if they maintain and continue investing into the existing tooling, as they are running in quite a few places already, especially dbt core running in a self-managed way. I certainly hope they won't try to squeeze revenue for the sake of it from their combined users.
It's an interesting time to be in the space, and it feels great to be one of the few independent players in the market.
They specifically mention the dbt-core will remain open source and will be supported. However this type of consolidation will very likely bring increased prices.
I hope Fivetran alternatives like dlt remain open source.
Not that surprising. How many millions has been piled into dbt Labs?
dbt Cloud is an uncompelling product. Fivetran is convenient but absurdly expensive. Now dbt core development is just another marketing cost for Fivetran.
I'd really love to see everyone move on from the "modern data stack". dbt is a sticking plaster to a problem caused by orgs aggressively adopting SaaS products and choosing distributed software architectures without any thought about how to deal with the problem that your data models don't fit together and data is stored in 30 different places.
Would like to echo the absurdly expensive bit. They charged us over $140k / year to basically stream ~1TB of production data (50m rows) from our Postgres into a redshift database.
They were part of the reason I started https://canine.sh as a way to self host more things. Had we had the confidence to host Airbyte or kafka + debezium as an option, we wouldn't have to deal with the atrocious pricing.
Everytime I bring up self hosting, the response is always a rote response of: "Its way too hard", "What if it goes down?", "Just delegate it to the professionals". To me this just seems incredibly defeatist, and its the same set of rebukes regardless if the cloud version costs $100 or $100,000.
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[ 0.20 ms ] story [ 29.4 ms ] threadWe are fully committed to open source dbt and don't want to build a 'walled garden'. Interoperability is one of the key value propositions of both Fivetran and dbt. While I'm biased, I think the main implications for users is that their favorite tooling will be with one vendor who cares about what makes them great.
You can read a bit more here: https://www.fivetran.com/blog/the-era-of-open-data-infrastru...
last update we had was Fivetran had 200m in 2023 and 300m in 2024 (https://www.fivetran.com/press/fivetran-surpasses-300m-arr-d...)
if Fivetran continued at same pace in 2025, that means it's at 400m ish and dbt was at 200m. not bad for dbt.
The "modern data stack" market excluding of data warehouse / data lake is pretty small. Fivetran is biggest one and still under $500M in revenue, so they're acquiring other parts and starting to offer their own datalake (managed Iceberg).
Snowflake started offering fivetran-like connectors a couple years ago and I expect they'll double down there. Same with Databricks. Microsoft has Fabric now, but the reviews have been terrible (my experience included).
I think they'll each ultimately have a full data stack.
If you don't want to wait, we built a modern-data-stack-in-a-box at Definite (https://www.definite.app/).
But I still feel like I'm missing, in DBT, capabilities for DB DDL/DML deployment (which I've done from Liquibase in DBT) for a fully CICD modern data stack. Preconditions, post-conditions, only-deploy-changed-code capabilities... Am I missing something in DBT?
I wonder who's next to really consolidate their platform play and compete with the old legacy MDM provider like Informatica. Data Observability or Catalog like Monte Carlo and Atlan. The whole Modern Data Stack has either died, acquired or merged by now. Wonder what's missing for Fivetran to IPO too.
I also wonder what this merge means for Airbyte who raised 150m at 1.5b in 2023.
I think consolidation in the space has been coming for quite some time now and this merger only confirms what us, along with many others, have been saying: the data tooling is in a miserable state and we had to glue together a bunch of different tools that don't work with each other.
At this point, I think it is quite obvious that Fivetran is going for Snowflake/Databricks's market share. They own the ingestion for many companies already, and they will offer a managed data lake product in order to compete with the data giants. By owning the means of bringing the data in (Fivetran) as well as the transformation layer (dbt/sqlmesh) they will aim to get ahead of Snowflakes of the world.
I think it'll be a win for the data community if they maintain and continue investing into the existing tooling, as they are running in quite a few places already, especially dbt core running in a self-managed way. I certainly hope they won't try to squeeze revenue for the sake of it from their combined users.
It's an interesting time to be in the space, and it feels great to be one of the few independent players in the market.
I hope Fivetran alternatives like dlt remain open source.
dbt Cloud is an uncompelling product. Fivetran is convenient but absurdly expensive. Now dbt core development is just another marketing cost for Fivetran.
I'd really love to see everyone move on from the "modern data stack". dbt is a sticking plaster to a problem caused by orgs aggressively adopting SaaS products and choosing distributed software architectures without any thought about how to deal with the problem that your data models don't fit together and data is stored in 30 different places.
They were part of the reason I started https://canine.sh as a way to self host more things. Had we had the confidence to host Airbyte or kafka + debezium as an option, we wouldn't have to deal with the atrocious pricing.
Everytime I bring up self hosting, the response is always a rote response of: "Its way too hard", "What if it goes down?", "Just delegate it to the professionals". To me this just seems incredibly defeatist, and its the same set of rebukes regardless if the cloud version costs $100 or $100,000.
end of rant.