Hevo Data vs dbt Labs
Side-by-side comparison of AI visibility scores, market position, and capabilities
No-code data pipeline and real-time ELT platform; San Francisco/Bengaluru; raised $42M+ from Sequoia India; supports 150+ data sources;
About
Hevo Data is a no-code data pipeline and real-time ELT platform founded in 2017 and headquartered in San Francisco, California, with core engineering in Bengaluru, India. The company was founded by Manish Jethani and Sourabh Agarwal to provide a simpler alternative to complex, code-heavy data pipeline tools for data teams that do not have dedicated data engineering resources. Hevo's platform enables analysts and non-engineers to connect data sources, define transformations using a visual interface or Python, and load data into cloud data warehouses without writing connector code or managing pipeline infrastructure.\n\nHevo raised $42 million in funding from investors including Sequoia Capital India, Qualgro, and Unusual Ventures. The platform supports more than 150 data sources including databases, SaaS applications, advertising platforms, payment processors, and custom webhooks. Its real-time data pipeline engine processes and delivers data with sub-minute latency for streaming sources, making it suitable for analytics use cases that require near-real-time freshness. Hevo's automatic schema management handles changes in source schemas without pipeline failures, addressing one of the most common maintenance burdens for data teams.\n\nHevo positions itself as a cost-effective alternative to Fivetran and Stitch for mid-market companies and growing data teams that need broader connector coverage at lower price points. The platform's transformation capabilities include a visual mapping interface for simple field transformations, a Python transformer for complex data manipulation, and dbt integration for warehouse-native transformations. Hevo is particularly popular in the Asia-Pacific market and among companies with significant SaaS-to-warehouse integration needs.
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;
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.
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