dbt Labs vs Hex Technologies
Side-by-side comparison of AI visibility scores, market position, and capabilities
Analytics engineering company that created dbt and established the discipline as a category; Oct 2025 all-stock merger with Fivetran announced; acquired SDF Jan 2025; dbt open-source framework is the de facto standard for SQL-based data transformation.
About
dbt Labs is a data transformation and analytics engineering company founded in 2016 and headquartered in Philadelphia, Pennsylvania, that created dbt (data build tool) — the open-source framework that established analytics engineering as a discipline and became the de facto standard for transforming raw data in the modern data warehouse. The company was founded by Tristan Handy, Drew Banin, and Connor McArthur with the conviction that data analysts should have the same software engineering workflows — version control, testing, documentation, modularity — that application engineers take for granted. dbt brought those practices to SQL-based data transformation, enabling data teams to build reliable, maintainable data pipelines.\n\nThe dbt product ecosystem includes dbt Core (the open-source transformation framework), dbt Cloud (the managed development and deployment platform), dbt Explorer (data lineage and documentation), and a growing set of features for data governance and collaboration. In January 2025, dbt Labs acquired SDF Labs, a high-performance SQL compilation and semantic layer technology, deepening its capabilities in query planning and column-level lineage. dbt integrates natively with major cloud data warehouses including Snowflake, Databricks, BigQuery, and Redshift, and sits at the center of the modern data stack alongside ingestion tools like Fivetran and orchestration platforms like Airflow.\n\nIn October 2025, dbt Labs announced an all-stock merger with Fivetran, a combination that would unite the leading data ingestion and transformation layers of the modern data stack under one company. dbt Core's open-source community spans hundreds of thousands of data practitioners globally, and dbt Cloud serves thousands of paying enterprise customers. The merger, if completed, would create a dominant end-to-end data pipeline company and redefine the competitive landscape in the modern data stack market.
San Francisco CA collaborative data workspace; raised $52M+; combines SQL, Python, and AI in a notebook-style environment for data teams and stakeholders.
About
Hex Technologies is a data workspace and collaborative analytics platform founded in 2021 and headquartered in San Francisco, California. The company was founded by Barry McCardel and Caitlin Colgrove to build a modern analytics environment that feels natural to data scientists and analysts but produces outputs that business stakeholders can actually consume. Traditional Python notebooks like Jupyter are powerful for analysis but produce outputs that non-technical users cannot easily explore or interact with. Hex bridges this gap by enabling analysts to write SQL and Python in a notebook-style interface and publish the results as interactive data apps.\n\nHex raised $52 million in funding from investors including Andreessen Horowitz, Redpoint Ventures, and Bain Capital Ventures. Its platform provides a shared, cloud-hosted notebook environment where data teams collaborate on analyses in real time — multiple analysts can work in the same project simultaneously, similar to Google Docs for data work. Projects can be published as interactive data apps with filters, dropdowns, and visualizations that business users can explore without needing to understand the underlying code. This analytics-to-app publishing workflow makes Hex a practical replacement for both ad hoc analysis in notebooks and static dashboard tools.\n\nHex's AI capabilities include Magic, an AI coding assistant that helps analysts write SQL and Python, explain unfamiliar code, generate transformations from natural language descriptions, and debug errors. The platform connects to Snowflake, BigQuery, Redshift, Databricks, DuckDB, and major databases. Its versioning and scheduling capabilities bring production-grade reliability to analysis projects, and its workspace collaboration features make it well-suited for analytics engineering teams at data-driven companies.
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