{"id":410457,"date":"2025-10-16T22:00:44","date_gmt":"2025-10-16T22:00:44","guid":{"rendered":"https:\/\/distillery.com\/blog\/fivetran-dbt-una-fusion-que-podria-remodelar-tu-pila-de-datos-en-2025\/"},"modified":"2026-04-22T20:00:07","modified_gmt":"2026-04-22T20:00:07","slug":"fivetran-dbt-una-fusion-que-podria-remodelar-tu-pila-de-datos-en-2025","status":"publish","type":"post","link":"https:\/\/distillery.com\/es\/blog\/fivetran-dbt-una-fusion-que-podria-remodelar-tu-pila-de-datos-en-2025\/","title":{"rendered":"Fivetran + dbt: Una fusi\u00f3n que podr\u00eda remodelar tu pila de datos en 2025"},"content":{"rendered":"\n

When two of the most widely used tools in modern analytics, Fivetran<\/strong><\/a> (for ingestion) and dbt Labs<\/strong><\/a> (for transformation), decide to merge, it\u2019s more than industry news. For small and mid-sized companies, it raises important questions about how to build scalable, maintainable, and cost-efficient data stacks.<\/p>\n\n

What parts of your stack are now redundant? Which areas become more efficient, or more risky? And how should teams think about their next move in a post-merger landscape?<\/p>\n\n

<\/p>\n\n

What Happened<\/h2>\n\n

On October 13, 2025, Fivetran announced an all-stock merger with dbt Labs<\/a>, uniting two of the most popular components of the modern data stack. The combined company is projected to reach approximately $600 million in annual recurring revenue<\/a> and will operate under a joint leadership structure: George Fraser (Fivetran) as CEO and Tristan Handy (dbt) as President.<\/p>\n\n

The companies describe their shared vision as \u201copen data infrastructure,\u201d a unified ecosystem that simplifies data movement, transformation, and activation without locking users into a single compute engine. dbt Core will remain open source, preserving flexibility and community contributions, while both platforms continue to operate independently until the deal officially closes.<\/p>\n\n

Two main factors drove this merger:<\/p>\n\n

    \n
  1. Consolidation pressure.<\/strong> Companies are demanding simplified, end-to-end data workflows with fewer integration points and less maintenance.<\/li>\n\n\n\n
  2. AI readiness.<\/strong> Reliable, low-latency pipelines are becoming mission-critical as organizations look to operationalize AI and analytics.<\/li>\n<\/ol>\n\n

    The merger also reflects a broader shift in the data tooling market. As competition has intensified, with lakehouse platforms expanding upstream and open-source tools gaining traction, Fivetran and dbt have moved to strengthen their position by uniting. It\u2019s a strategic decision that consolidates two category leaders into a single, more comprehensive offering designed to hold the middle ground in an increasingly crowded ecosystem.<\/p>\n\n

    <\/p>\n\n

    What This Means for Growing Data Teams<\/h2>\n\n

    The merger promises efficiency and simplification. Tighter integration could reduce the need for \u201cglue code\u201d and manual orchestration between ingestion and transformation layers. But there are tradeoffs to consider.<\/p>\n\n

    Potential Advantages<\/strong><\/td>Possible Drawbacks<\/strong><\/td><\/tr>
    Seamless data flow between ingestion and transformation<\/td>Increased vendor lock-in and consolidation of control<\/td><\/tr>
    Less operational overhead and faster implementation<\/td>Reduced flexibility for unique or complex workflows<\/td><\/tr>
    Shorter time to insights for standard use cases<\/td>Potential deprioritization of advanced or custom features<\/td><\/tr><\/tbody><\/table><\/figure>\n\n

    For example, a small team that currently uses Fivetran for EL, dbt for T, and Airflow to coordinate dependencies could see that middle layer shrink. The ingestion-to-transformation path may become almost plug-and-play, freeing up time to focus on modeling and analytics rather than pipeline maintenance. However, custom orchestration (such as multi-domain triggers or mixed workloads) will still require tools like Airflow<\/a> or Dagster<\/a>.<\/p>\n\n

    <\/p>\n\n

    Should Teams Still Write End-to-End DAGs?<\/h2>\n\n

    The short answer: only when they add real value.<\/p>\n\n

    The Fivetran\u2013dbt merger doesn\u2019t eliminate the need for orchestration, but it does shift when and why it\u2019s necessary. Here\u2019s how the main approaches compare:<\/p>\n\n

    1. Managed Stack (Fivetran + dbt Cloud)<\/strong><\/h4>\n\n

    Best for:<\/strong> Lean teams prioritizing speed and simplicity.<\/p>\n\n