Coopetition in Cloud Data Platforms
Tristan Handy, CEO of dbt Labs, on dbt’s multi-cloud tailwinds
Coopetition is the default structure of infrastructure markets because the same company can be both a distribution channel and a product threat at the exact same time. dbt depends on warehouses and clouds to run customer workloads, while those same platforms keep moving up the stack into transformation, orchestration, governance, and AI. That is not a temporary contradiction. It is how modern data platforms are sold, integrated, and expanded inside enterprises.
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The warehouse became the center of gravity first, then the surrounding tool ecosystem formed around it. Redshift launched in 2013, Snowflake in 2014, dbt in 2016, and tools like Fivetran and Census plugged into the same SQL based hub. That shared hub makes interdependence unavoidable.
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dbt is a good example of why this persists. Teams adopt dbt to define business logic once, test it, document it, and run it across warehouses, while Snowflake and Databricks try to pull those workflows into their own native stacks. dbt still benefits when those platforms grow, because more warehouse usage creates more transformation work.
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The same pattern shows up beyond analytics. AWS sells Amazon MSK, a managed Kafka service, while partners like Confluent sell on top of cloud infrastructure and alongside cloud sales channels. In practice, infrastructure buyers want the best tool for each layer, so platforms keep partnering even when roadmaps overlap.
This dynamic pushes the market toward control planes and open standards that sit above any single vendor. As warehouses, clouds, and adjacent tools keep rebundling, the winners will be the products that can plug into every major platform, preserve portability, and remain valuable even when partners become direct competitors.