Fivetran and dbt Rebundle ELT Stack
Fivetran
This merger turns the best known point products in the modern data stack into a single vendor that can own the path from raw source data to modeled business tables. Fivetran already sat at the start of the workflow, pulling data out of SaaS apps and databases, while dbt owned the transformation layer where analytics engineers define tests, models, and business logic. Putting them together gives the combined company a stronger case for selling one platform, not two tools, as enterprises consolidate vendors and AI pushes more spending into core data infrastructure.
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The combination is a direct rebundling move. Data teams used to buy Fivetran for extract and load, dbt for transform, Snowflake or Databricks for storage and compute, and other products for orchestration or activation. Recent research across the category shows buyers now want fewer contracts and fewer hand built integrations, which makes an integrated ELT stack more attractive.
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The revenue math shows why this matters. Fivetran was at about $325M ARR at the end of 2024, while dbt Labs was at about $100M. The merger announcement said the combined company would approach $600M ARR, implying meaningful 2025 growth plus added scale from Fivetran's earlier Census deal, which extended it from ingestion into downstream activation.
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Strategically, this gives the company a cleaner answer to pressure from both sides. Warehouses like Snowflake and Databricks keep moving up into pipelines and transformation, while niche tools attack single slices of the stack. A unified Fivetran and dbt can argue that customers keep open standards like SQL, Iceberg, and dbt Core, but buy one operating layer for movement, transformation, metadata, and activation.
The next step is broader platform consolidation around the data control plane. With ingestion, transformation, and activation under one roof, the combined company is positioned to add more governance, orchestration, and possibly compute adjacent products, aiming to become the default layer enterprises use to build analytics and AI workflows across multiple clouds.