Transformations that know their context

dlthub provides two distinct methods for running data transformations: using dltHub's native transformations or integrating dbt projects within dltHub's framework. These methods address the evolving needs of data teams, offering flexibility for both centralized and decentralized collaboration patterns. The native dltHub transformations leverage agents to handle complex architectural contexts and metadata, thus reducing maintenance efforts and enabling non-senior team members to perform at a high level. This approach allows for seamless integration of various data processing tools and architectures, such as data mesh and AI-native teams, by maintaining a single runtime environment that ensures consistency and context retention. Alternatively, teams with existing dbt projects can continue to use dbt with dltHub, either by running dbt Core jobs or triggering dbt Cloud jobs post-ingestion, thus preserving the existing workflow while benefiting from event-based orchestration that eliminates scheduling uncertainties and race conditions.

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