Open Data Infrastructure needs context engineering to unify data use cases

Open Data Infrastructure aims to support diverse data use cases through interoperable systems and a unified source of truth, but the article argues that this also requires context engineering to make data understandable, trustworthy, and retrievable for both people and AI. Fivetran and dbt Labs position the Managed Data Lake Service as an approach for continuously synchronizing source data into open table formats, managing schema changes, and publishing governed metadata for use across compute engines. Context engineering addresses four areas: defining data semantics and provenance, validating quality and operational state, governing access and lifecycle changes, and delivering only the relevant machine-readable context to specific users or AI agents. The article highlights dbt’s Semantic Layer, Apache Ossie, dbt Catalog, Discovery API, dbt Mesh, and MCP server as tools and standards for documenting business meaning, lineage, testing, freshness, governance, discovery, and AI integration. By externalizing institutional knowledge and making it explicit, organizations can improve self-service, collaboration, reproducibility, onboarding, and the reliability of decisions and automated systems.

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