In a move that's sending ripples across the data world, Fivetran and dbt Labs have announced they are merging.
For years, data teams have stitched together Fivetran and dbt as core parts of their ELT (Extract, Load, Transform) pipelines. Fivetran handles the "EL" - getting data from hundreds of sources into a cloud warehouse. Then, dbt takes over for the "T" - transforming that raw data into clean, reliable, and business-ready datasets.
This move is seen by some as just them “catching up” to players like Databricks. What do you think?
They are currently committed to keeping dbt Core as Open Source (though there is some skepticism from some in the community).
Why it Matters for Analysts, Data Scientists, Data Engineers
For the individual contributors on the front lines, this is a big deal if you’re using dbt and/or Fivetran. Your daily workflow should get a lot more streamlined.
- A More Unified Workflow: Say goodbye to the context-switching between two separate platforms. A unified interface means less friction and a more intuitive process for managing data from ingestion to transformation. To quote dbt CEO/Founder Tristan Handy, “it all just works together, no duct tape”.
- Deeper Integrations: Expect to see tighter integrations, like Fivetran sources being automatically available in your dbt projects or the ability to trigger Fivetran syncs directly from a dbt run. This level of integration simplifies lineage and debugging, making it easier to trust your data.
- Skill Set Evolution: While this simplifies the tooling, it also raises the bar. Proficiency in both Fivetran and dbt will likely become a standard expectation, if not already. If you've specialized in one, now is the time to get comfortable with the other.
Why it Matters for Analytics Leaders
For those managing data teams and strategy, this merger changes the approach for building and scaling your data stack.
- Simplified Vendor Management: Managing one vendor instead of two means streamlined procurement, support, and billing. This reduces administrative overhead and simplifies your tech stack, making it easier to manage and govern.
- Potential for Cost Savings (and Increases): A single, bundled offering could present a more attractive price point than two separate contracts. However, with less competition, there's also the long-term risk of price increases. Leaders will need to carefully evaluate the total cost of ownership.
- Accelerated Time to Value: A fully integrated platform promises to drastically reduce the time and engineering resources required to set up and maintain data pipelines. This allows your team to spend less time on infrastructure and more time delivering business value and strategic insights. It's a powerful argument for accelerating your company's data maturity.
Mammoths like Databricks have already set the standard for a one stop shop. Is this move just Fivetran and dbt playing catch up? Or will this enable something bigger?
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