Composable canonicals: from tribal data knowledge to versioned artifact

The text explores a new AI-native architecture that addresses the traditional central data team problem by decentralizing knowledge and ownership to domain teams, leveraging large language models (LLMs) to enhance technical capabilities and knowledge transfer. It introduces the concept of "composable canonicals," which are self-documented knowledge blocks that combine raw data and inferred schemas into a machine-readable form, allowing domain knowledge to be preserved and utilized across departments. The architecture distinguishes between source canonicals, which model individual systems in their own language, and business canonicals, which integrate these source models into a unified view to answer complex cross-system questions. This approach is exemplified by combining data from systems like HubSpot, Slack, and Luma into a comprehensive customer table, enabling consistent and accurate analysis. The text also highlights the role of dltHub in supporting this workflow from data ingestion to production, providing automation and orchestration to streamline operations.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.