Context Layer for Databricks

A context layer for Databricks is the governed layer of business meaning - definitions, relationships, lineage, and policy - that turns the data in your Databricks lakehouse into something AI agents can understand and use trustworthily. Databricks provides a powerful native foundation here in Unity Catalog, its unified governance layer for data and AI. Understanding what Unity Catalog delivers - and where a context layer needs to reach beyond a single platform - is central to grounding enterprise AI on Databricks data.

It matters because Databricks is where many organizations run both their data and their AI, and the lakehouse pattern mixes structured tables, files, ML features, and models in one place. An AI agent operating over that estate needs to know what the data means in business terms, whether it is trustworthy, and how it connects - context that the raw lakehouse does not supply on its own. A context layer provides it, and the most useful one spans Databricks together with the rest of the systems the business depends on.

A Context Layer for Databricks

Within Databricks, a context layer means surrounding lakehouse assets - tables, views, ML features, and models - with the metadata that gives them meaning and trust: business definitions for each metric and term, lineage showing how data flowed through notebooks and pipelines, classification of sensitive data, and access policy. With that in place, an AI agent querying the lakehouse can interpret results correctly and stay within governance, rather than improvising from technical names.

Databricks has made governance native rather than an afterthought through Unity Catalog, so any context-layer discussion for Databricks starts there.

Databricks Unity Catalog

Unity Catalog is the unified governance layer built into Databricks; once enabled, it operates beneath every data interaction - enforcing access control, tracking lineage, and logging activity automatically. Its capabilities map closely onto a context layer's foundations:

• Unified governance for data and AI. One governance model across tables, files, ML features, and models, with access control and auditing built in.
• Automated column-level lineage. End-to-end lineage from source to dashboards, simplifying impact analysis and AI audits - and external lineage (preview) extends the graph to upstream sources (e.g. Salesforce, MySQL) and downstream tools (e.g. Tableau, Power BI).
• Discovery, quality, and sharing. A unified view of data and AI assets, with discoverability, quality, and governed sharing.
• Open source. Databricks open-sourced Unity Catalog under the Linux Foundation in June 2024; the OSS project is API-compatible with the managed service and integrates with Apache Spark, Trino, Apache Iceberg, DuckDB, and more.

This is a strong, increasingly open governance foundation for data and AI inside Databricks.

Genie & the Need for Context

The need for a context layer on Databricks is sharpest with AI features like Databricks AI/BI Genie, which lets users ask natural-language questions of their data. Genie - like any natural-language-to-data interface - is only as accurate as the business context behind it: it needs to know what "active customer" means, which table is authoritative, and how metrics are defined, or it will produce confident but wrong answers. Unity Catalog supplies technical governance and lineage; the missing piece is the curated business meaning - the glossary definitions and relationships - that lets natural-language AI map a question to the right, trustworthy data. That business layer is exactly what a context layer adds on top of Unity Catalog.

The Gap: Beyond Databricks

Unity Catalog governs Databricks well, and its open-sourcing and external-lineage features push it outward - but a complete context layer still has to address two things a platform-centred catalog only partly covers:

• Business meaning, curated. Unity Catalog excels at technical governance and lineage; the curated business definitions, glossary, and human-owned context that AI needs to interpret data still have to be layered on and governed deliberately.
• The whole estate, and any agent. Most enterprises also run Snowflake, dbt, BI tools, and operational systems; a business term and its lineage often span all of them. And AI agents that aren't Databricks-native need governed context through an open standard - the Model Context Protocol.

Left unaddressed, these turn each tool into a context island - the AI-era equivalent of the data silos enterprises spent the 2000s fighting - where Databricks knows its slice, Snowflake knows its slice, and no agent sees the whole. The complete context layer spans those islands: Databricks and everything around it, with curated business meaning on top.

How Dawiso Fits

Dawiso is the cross-platform, business-aware context layer that closes the gaps Unity Catalog leaves - with Databricks as a first-class source within it, not a competitor. It connects to Databricks alongside 40+ other platforms and adds what AI needs on top of native governance:

• Curated business meaning. The business glossary defines each term once - across Databricks and everything else - turning the institutional knowledge in wikis and people's heads into governed context.
• Cross-platform, governed end-to-end. Interactive data lineage traces flows from source systems through dbt into the lakehouse and out to BI - unifying with, not duplicating, Unity Catalog's technical lineage - while classification and policy stay consistent across the estate rather than stopping at the workspace edge.
• Served to any agent via open MCP. The Context Layer delivers this governed context to any AI agent or copilot through the open MCP Server.

Unity Catalog keeps governing the lakehouse natively; Dawiso gives your AI the curated, cross-platform business context that a single-platform catalog cannot - the difference between an agent that understands Databricks and one that understands your business.

Conclusion

A context layer is what turns Databricks data into something AI can use trustworthily, and Databricks has built a strong, increasingly open foundation in Unity Catalog - unified governance, column-level lineage, and external lineage that reaches beyond the lakehouse. What remains is the business meaning AI needs to interpret that data, and the reality that the business runs on more than one platform. The complete context layer adds curated definitions and relationships on top of Unity Catalog, unifies Databricks with the rest of the estate, and serves governed context to any agent - including Genie - through open MCP. Govern the lakehouse natively, then give your AI the business context to understand it.