---
title: "The 12 Best AI-Ready Data Platforms in 2026"
url: "https://atlan.com/know/ai-agent/data-for-ai/best-ai-ready-data-platforms/"
description: "Compare the best AI-ready data platforms of 2026: 12 ranked on whether a semantic layer reaches AI agents through a GA route, from Databricks to Snowflake."
author: "Emily Winks"
author_role: "Data Governance Expert"
published: "2026-10-28T00:00:00.000Z"
updated: "2026-10-28T00:00:00.000Z"
---

## What is the best AI-ready data platform in 2026?

There is no single best AI-ready data platform. On documented capability, Databricks, Google Cloud, Microsoft Fabric and Snowflake rank highest because each pairs an executable semantic layer with a generally available agent route that uses it: Genie One MCP over metric views, the Looker MCP server over LookML, Fabric IQ MCP over Power BI semantic models, and Cortex Analyst over semantic views. Dremio and Atlan follow, and Atlan publishes one set of definitions into several platforms.

The 3 checks used in this ranking:

- **Executable meaning layer** - semantic views, metric views or semantic models that compute measures and joins
- **Agent route that uses it** - an MCP server or endpoint documented as applying that layer, and its GA status
- **Reach into other vendors' data** - whether the meaning layer can be defined over data another vendor stores

## Key takeaways

- Databricks, Google Cloud, Microsoft Fabric and Snowflake each pair a semantic layer with a GA agent route that applies it.
- Databricks, Google Cloud and Fabric also document definitions over data that other vendors store.
- A glossary describes data; a semantic layer computes metrics, which is what stops an agent from guessing.
- Miro saw more than 65% wrong answers when agents queried 10,000+ tables with no semantic layer.

---

An AI-ready data platform is one where your metric definitions reach the agents that query it. Of 12 options ranked, Databricks, Google Cloud, Microsoft Fabric and Snowflake pair a semantic layer with a GA agent route; Dremio and Atlan follow.

### Audit whether your data is ready for an agent

Takes a business question and the tables behind it and returns which definitions, owners and access policies an agent would be missing. [Read the skill](/skills/ai-ready-data-audit.md).

*Paste into a new chat*

```
Use the skill at https://atlan.com/skills/ai-ready-data-audit.md to audit whether the data behind a business question is ready for an AI agent. Ask me for whatever it needs.
```

*Run once in a terminal*

```
curl -fsSL --create-dirs \
  -o ~/.agents/skills/ai-ready-data-audit/SKILL.md \
  https://atlan.com/skills/ai-ready-data-audit.md
```

*For an agent*

```
curl -fsSL https://atlan.com/skills/ai-ready-data-audit.md
```


    Human
    Agent


  ClaudeChatGPTGeminiCursorOther
  Copy

---

When Miro's data team pointed AI agents at more than 10,000 tables with no semantic layer to guide them, the agents got the wrong answer more than 65% of the time. Miro organized its data into well-defined data products that constrain what agents can see, and added a context layer that draws on metadata, entity relationships and query history ([VentureBeat, 2026](https://venturebeat.com/data/sql-query-logs-hold-the-context-ai-agents-need-to-stop-hallucinating-joins)). An agent route is only as good as the meaning behind it. According to a 2026 study by COMPUTERWOCHE and Lufthansa Industry Solutions, 84% of C-level executives consider their data platforms AI-ready, while only just over half of business-area respondents agree ([LHIND, 2026](https://www.lufthansa-industry-solutions.com/de-en/studies/study-ai-ready-data-platforms-2026)).

- **Since late 2025:** most major platforms ship an MCP server or managed agent endpoint, and four on this list pair a GA one with an executable semantic layer.
- **Reach is widening:** Databricks, Google Cloud and Microsoft Fabric document definitions over data that other vendors store.
- **Statuses move fast:** Google's Looker MCP server went GA on October 26, 2026, two days before this ranking.

| Fact | Detail |
|---|---|
| Options ranked | 12: 11 data platforms and Atlan, a context layer across them |
| Ranking test | Executable meaning layer, then a GA agent route that uses it, then reach into other vendors' data |
| Semantic layer and GA agent route documented together | Databricks, Google Cloud, Microsoft Fabric, Snowflake |
| Evidence | Vendor product docs, release notes and launch announcements, opened 2026-10-28 |

---

## What is an AI-ready data platform?

An AI-ready data platform stores and serves data and also supplies the business meaning an agent needs to use it correctly: measures, entities, joins and the access policies that apply. Storage, compute and unstructured-data support still matter, and competing guides compare them well. Meaning is the axis they skip, so it is the one ranked here.

The reason is mechanical. An agent that sees only schemas has to guess what "active customer" or "net revenue" means, then guess which join produces it. Pramod Sadalage, Distinguished Engineer at Thoughtworks, and Prem Chandrasekaran put it plainly: "The semantic model doesn't make the agent smarter. It stops it from guessing" ([martinfowler.com, 2026](https://martinfowler.com/articles/making-data-ready-for-agentic-ai.html)).

Three things make a platform ready for agents:

- **An executable meaning layer:** semantic views, metric views, semantic models or LookML that define measures and joins an engine computes. A glossary or a tag describes data; a semantic layer computes it.
- **An agent route that uses it:** an MCP server or managed endpoint documented as applying that layer, with the caller's permissions.
- **Reach beyond itself:** a meaning layer that can be defined over data another vendor stores.

Four of the 12 options document the first two together at GA. For the full set of checks, see [what makes data AI-ready](https://atlan.com/know/ai-agent/data-for-ai/what-makes-data-ai-ready/) and the [AI-ready data checklist](https://atlan.com/know/ai-agent/data-for-ai/ai-ready-data-checklist/).





---

## How do the 12 options compare at a glance?

The 12 options below are ordered by the test in the next section, not by market share. Statuses are as each vendor's own docs, release notes or launch announcement gave them on 2026-10-28.

| Rank | Platform | Executable meaning layer | Agent route that uses it (status) | Best for |
|---|---|---|---|---|
| 1 | Databricks | Unity Catalog metric views | Genie One MCP (GA), grounded in metric views | Metric definitions agents query through Genie |
| 2 | Google Cloud | LookML in Looker | Looker-managed MCP server (GA, October 2026); BigQuery MCP server (GA) | LookML definitions served to agents through Looker |
| 3 | Microsoft Fabric | Power BI semantic models (DAX) | Fabric IQ MCP (GA), runs DAX against semantic models | Power BI semantic models served to agents |
| 4 | Snowflake | Semantic views (GA) | Snowflake-managed MCP server (GA), via Cortex Analyst | Semantic views served to agents through Cortex Analyst |
| 5 | Dremio | AI Semantic Layer | Dremio MCP server, hosted and self-hosted | One semantic layer over federated sources |
| 6 | Atlan | Context repository, rendered into each engine's semantic format | Atlan MCP server; Context Engineering Studio in Atlan Labs | One set of definitions published to several platforms |
| 7 | AWS | DataZone business glossary; Amazon Quick Topics | AWS MCP Server (GA) | A GA agent route across AWS services |
| 8 | Oracle Autonomous AI Database | Select AI comments and annotations | Built-in MCP server (released December 2025) | Agent access to Oracle Database |
| 9 | Teradata VantageCloud | Logical data models | Teradata MCP Server Community Edition (available since 2025), Enterprise MCP | Agents on existing Teradata analytics |
| 10 | Starburst | Data products | Starburst MCP server, read-only SQL and data product tools | Governed data products across warehouses |
| 11 | IBM watsonx.data | Semantic Automation Layer | watsonx.data intelligence MCP server, with Text-to-SQL | Automated enrichment in IBM Knowledge Catalog estates |
| 12 | Cloudera | Data Catalog glossaries | MCP Server for Apache Iceberg (preview at launch) | Agent access to Iceberg on hybrid infrastructure |

---

## How did we rank the best AI-ready data platforms?

We ranked every option on documented capability, read from vendor product docs, release notes and launch announcements, opened on 2026-10-28. We did not run agents against the platforms, so treat the ranking as a reading of what each vendor documents, not a benchmark.

### Check 1: Is there an executable meaning layer?

A named object that defines measures, entities or joins an engine computes counts: Databricks metric views, LookML, Power BI semantic models, Snowflake semantic views, Dremio's semantic layer and Atlan's context repository, which renders into those formats. Glossaries, enrichment labels, annotations and data products describe data and help agents find it; they count toward Check 2, not Check 1.

### Check 2: Does a GA agent route use it?

The route and the meaning layer have to be documented together: Genie One MCP grounded in metric views, the Looker MCP server working with LookML models, Fabric IQ MCP running DAX against semantic models, Cortex Analyst applying semantic views through Snowflake's MCP server. We record a route as GA when the vendor's docs or release notes say so.

### Check 3: Can the meaning layer reach other vendors' data?

This is the tie-breaker. Databricks metric views can use foreign tables from Lakehouse Federation, Looker models connect to other vendors' warehouses, Fabric references external storage through OneLake shortcuts, Dremio federates sources such as Snowflake, and Atlan publishes one repository into Snowflake and Databricks.

### How the order works

Options that pass Checks 1 and 2 at GA rank first. Next come options that document both without a GA label, with an unlabelled route ahead of one in preview. Then come platforms whose agent route works over glossaries, annotations, data products or enrichment: GA or released routes first, preview last. Within a group, Check 3 breaks ties, then the alphabet.

---

## The 12 best AI-ready data platforms at a glance

1. [Databricks](#best-for-metric-definitions-agents-query-through-genie-databricks) for metric definitions agents query through Genie
2. [Google Cloud](#best-for-lookml-definitions-served-to-agents-through-looker-google-cloud) for LookML definitions served to agents through Looker
3. [Microsoft Fabric](#best-for-power-bi-semantic-models-served-to-agents-microsoft-fabric) for Power BI semantic models served to agents
4. [Snowflake](#best-for-semantic-views-served-to-agents-through-cortex-analyst-snowflake) for semantic views served to agents through Cortex Analyst
5. [Dremio](#best-for-one-semantic-layer-over-federated-sources-dremio) for one semantic layer over federated sources
6. [Atlan](#best-for-one-set-of-definitions-published-to-several-platforms-atlan) for one set of definitions published to several platforms
7. [AWS](#best-for-a-ga-agent-route-across-aws-services-aws) for a GA agent route across AWS services
8. [Oracle Autonomous AI Database](#best-for-agent-access-to-oracle-database-oracle-autonomous-ai-database) for agent access to Oracle Database
9. [Teradata VantageCloud](#best-for-agents-on-existing-teradata-analytics-teradata-vantagecloud) for agents on existing Teradata analytics
10. [Starburst](#best-for-governed-data-products-across-warehouses-starburst) for governed data products across warehouses
11. [IBM watsonx.data](#best-for-automated-enrichment-in-ibm-knowledge-catalog-estates-ibm-watsonx-data) for automated enrichment in IBM Knowledge Catalog estates
12. [Cloudera](#best-for-agent-access-to-iceberg-on-hybrid-infrastructure-cloudera) for agent access to Iceberg on hybrid infrastructure

---

## Best for metric definitions agents query through Genie: Databricks

Databricks keeps meaning in Unity Catalog metric views, and its Genie One MCP server is generally available. Genie One works from Genie Ontology, which draws on metric views, so the meaning layer and the GA agent route are documented together. Metric views can also be built on foreign tables, which Lakehouse Federation connects to Snowflake, Google BigQuery, Amazon Redshift and other sources.

**Pros:**
- Genie One MCP is generally available.
- Metric views define measures once in Unity Catalog, for SQL editors, notebooks, dashboards and Genie agents.
- Synonyms, display names and formatting rules in metric views are built to improve agent accuracy.
- Metric views can use foreign tables, so definitions can cover data held by other vendors.

**Good to know:**
- The workspace MCP endpoints for a Genie agent, AI Search and Unity Catalog functions are in Public Preview.
- Lakehouse Federation gives read-only access to external data.

Databricks documents metric views as "the core implementation of Unity Catalog semantics in Unity Catalog," with metadata to "improve agent accuracy" ([Azure Databricks docs](https://learn.microsoft.com/en-us/azure/databricks/uc-semantics/metric-views/)), and lists foreign tables among their supported sources ([Azure Databricks docs](https://learn.microsoft.com/en-us/azure/databricks/uc-semantics/metric-views/basic-modeling)). Its MCP docs state that "the Genie One MCP is generally available," while the workspace endpoints "are in Public Preview" ([Azure Databricks docs](https://learn.microsoft.com/en-us/azure/databricks/agents/mcp-tools/built-in-mcp-services)). Genie Ontology "combines modeled context you govern in Unity Catalog semantics, such as metric views, domains, and Pages" ([Azure Databricks docs](https://learn.microsoft.com/en-us/azure/databricks/genie-one/chat)), and Lakehouse Federation "provides governed, read-only access to external data through Unity Catalog foreign catalogs" ([Azure Databricks docs](https://learn.microsoft.com/en-us/azure/databricks/query-federation/)). More on the model: [Unity Catalog metrics](https://atlan.com/know/ai-agent/databricks/unity-catalog-metrics/) and [Unity Catalog's semantic layer](https://atlan.com/know/ai-agent/semantic-layer/unity-catalog-semantic-layer/).

| Check | What Databricks documents |
|---|---|
| Meaning layer | Unity Catalog metric views |
| Agent access | Genie One MCP, GA; workspace endpoints in Public Preview |
| Cross-vendor scope | Metric views over foreign tables from Lakehouse Federation |

**Choose Databricks if** your certified metrics live in Unity Catalog and your agents can reach them through Genie. Compare it with a [cross-platform semantic layer](https://atlan.com/know/ai-agent/semantic-layer/unity-catalog-vs-cross-platform-semantic-layer/) if other platforms are in play.

**Sources:** [Metric views](https://learn.microsoft.com/en-us/azure/databricks/uc-semantics/metric-views/) / [Built-in MCP services](https://learn.microsoft.com/en-us/azure/databricks/agents/mcp-tools/built-in-mcp-services) / [Lakehouse Federation](https://learn.microsoft.com/en-us/azure/databricks/query-federation/)

---

## Best for LookML definitions served to agents through Looker: Google Cloud

Google keeps meaning in LookML, Looker's semantic modeling language, and the Looker-managed MCP server became generally available on October 26, 2026, giving agents access to LookML models. Looker connects to a wide range of databases, including Amazon Redshift, Databricks and Snowflake, so LookML definitions can sit over other vendors' warehouses. For SQL work in BigQuery itself, the BigQuery MCP server is generally available too.

**Pros:**
- The Looker-managed MCP server is GA and works with LookML models.
- The BigQuery MCP server is GA for examining resources, generating SQL and running queries.
- Looker supports many SQL dialects, so one model can cover more than one warehouse.
- BigQuery Omni runs BigQuery analytics on data stored in Amazon S3 and Azure Blob Storage.

**Good to know:**
- Agents reach LookML definitions through the Looker MCP server; the BigQuery MCP server serves SQL against BigQuery.

LookML is "the language that is used in Looker to create semantic data models" ([Google Cloud](https://docs.cloud.google.com/looker/docs/what-is-lookml)). Google's Looker release notes for October 26, 2026 state that "the Looker-managed Model Context Protocol (MCP) server is now generally available (GA)" ([Google Cloud](https://docs.cloud.google.com/looker/docs/release-notes)), and the server lets agents "securely connect to a Looker instance and interact with business data and LookML models" ([Google Cloud](https://docs.cloud.google.com/looker/docs/mcp)). "Looker supports a wide range of SQL database dialects," including Snowflake, Amazon Redshift and Databricks ([Google Cloud](https://docs.cloud.google.com/looker/docs/dialects)). BigQuery's release notes list the BigQuery MCP server as "Generally Available (GA)" ([Google Cloud](https://docs.cloud.google.com/bigquery/docs/release-notes)), and "BigQuery Omni is a multi-cloud analytics solution" for data in Amazon S3 or Azure Blob Storage ([Google Cloud](https://docs.cloud.google.com/bigquery/docs/omni-introduction)). For Google's catalog-side story, see [Google Knowledge Catalog](https://atlan.com/know/ai-agent/gcp/google-knowledge-catalog/).

| Check | What Google documents |
|---|---|
| Meaning layer | LookML in Looker |
| Agent access | Looker-managed MCP server, GA; BigQuery MCP server, GA |
| Cross-vendor scope | Looker connects to Snowflake, Amazon Redshift, Databricks and other databases |

**Choose Google Cloud if** your metric definitions live in LookML, or your agents already run on Google Cloud.

**Sources:** [What is LookML](https://docs.cloud.google.com/looker/docs/what-is-lookml) / [Looker MCP server](https://docs.cloud.google.com/looker/docs/mcp) / [Looker release notes](https://docs.cloud.google.com/looker/docs/release-notes) / [BigQuery MCP server](https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp)

---

## Best for Power BI semantic models served to agents: Microsoft Fabric

Microsoft Fabric pairs Power BI semantic models with Fabric IQ MCP, which is generally available and lets an agent inspect semantic models and run DAX against them with the caller's own permissions. OneLake shortcuts reference external data in place, including Amazon S3, Google Cloud Storage and Iceberg sources.

**Pros:**
- Fabric IQ MCP is generally available.
- MCP calls apply the caller's existing Fabric permissions.
- Agents run DAX against Power BI semantic models, so measures are computed, not guessed.
- Semantic models in Direct Lake mode can read shortcut data directly.

**Good to know:**
- The Fabric IQ ontology is in preview; semantic models are the GA path.
- Fabric IQ MCP is a read-only toolset.

Microsoft Learn describes Fabric IQ's two core items as "ontology (preview) and semantic model" and says Fabric IQ can "unify external operational data using OneLake shortcuts, referencing it in place without copying" ([Microsoft Learn](https://learn.microsoft.com/en-us/fabric/iq/overview)). It states that "Fabric IQ MCP is generally available," that the server "exposes a read-only toolset that helps an agent find Power BI content, inspect report and semantic model metadata, locate relevant values, and run DAX queries," and that it "applies the caller's existing Microsoft Fabric permissions" ([Microsoft Learn](https://learn.microsoft.com/en-us/fabric/iq/connectors/fabric-iq-mcp)). With Direct Lake, "Analysis Services can read data directly from the shortcut" ([Microsoft Learn](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts)). For how Fabric IQ relates to Microsoft's other context sources, see [Fabric IQ vs Work IQ vs Foundry IQ](https://atlan.com/know/microsoft-fabric/fabric-iq-vs-work-iq-vs-foundry-iq/).

| Check | What Microsoft documents |
|---|---|
| Meaning layer | Power BI semantic models (DAX); Fabric IQ ontology in preview |
| Agent access | Fabric IQ MCP, GA, read-only, runs DAX with the caller's permissions |
| Cross-vendor scope | OneLake shortcuts to Amazon S3, Google Cloud Storage, Iceberg and more |

**Choose Fabric if** Power BI semantic models already hold your metric definitions.

**Sources:** [Fabric IQ overview](https://learn.microsoft.com/en-us/fabric/iq/overview) / [Fabric IQ MCP](https://learn.microsoft.com/en-us/fabric/iq/connectors/fabric-iq-mcp) / [OneLake shortcuts](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts)

---

## Best for semantic views served to agents through Cortex Analyst: Snowflake

Snowflake stores business meaning in semantic views, schema-level objects that are generally available, with Semantic View Autopilot to help create them. Its Snowflake-managed MCP server, generally available since November 4, 2025, serves Cortex Analyst, Cortex Search and Cortex Agents as tools, and applies semantic views through Cortex Analyst.

**Pros:**
- Semantic views live in the database as schema-level objects.
- Semantic View Autopilot creates semantic views with AI assistance in Snowsight.
- One managed MCP server can serve Cortex Analyst, Cortex Search and Cortex Agents.

**Good to know:**
- Snowflake applies semantic views through the Cortex Analyst tool, and recommends exposing direct SQL on a separate MCP server so governed questions go through the semantic views.

Snowflake's docs say "you can store semantic business concepts directly in the database in a Semantic View, which is a schema-level object" ([Snowflake docs](https://docs.snowflake.com/en/user-guide/views-semantic/overview)), and its release notes state that "the ability to query semantic views is now generally available" ([Snowflake release notes](https://docs.snowflake.com/en/release-notes/2025/9_25)). "Semantic View Autopilot is the AI-assisted way to create a semantic view in Snowsight" ([Snowflake docs](https://docs.snowflake.com/en/user-guide/views-semantic/autopilot)). The MCP docs say "you can configure the MCP server to serve Cortex Analyst, Cortex Search, and Cortex Agents as tools" and that the server "only supports using semantic views with Cortex Analyst" ([Snowflake docs](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)). The November 4, 2025 release note announced "the general availability of the Snowflake-managed MCP server" ([Snowflake release notes](https://docs.snowflake.com/en/release-notes/2025/other/2025-11-04-cortex-agents-mcp)). Go deeper with [Snowflake semantic views](https://atlan.com/know/snowflake/snowflake-semantic-views/) and [Cortex Analyst vs custom text-to-SQL](https://atlan.com/know/snowflake/cortex-analyst-vs-text-to-sql/).

| Check | What Snowflake documents |
|---|---|
| Meaning layer | Semantic views (GA), Semantic View Autopilot |
| Agent access | Snowflake-managed MCP server, GA since November 4, 2025, via Cortex Analyst |

**Choose Snowflake if** your agents' questions resolve to Snowflake data and Cortex Analyst can be their route to the definitions. For context across more systems, see [a context layer for Snowflake](https://atlan.com/know/context-layer-for-snowflake/).

**Sources:** [Semantic views overview](https://docs.snowflake.com/en/user-guide/views-semantic/overview) / [Semantic View Autopilot](https://docs.snowflake.com/en/user-guide/views-semantic/autopilot) / [Snowflake-managed MCP server](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp) / [MCP server GA release note](https://docs.snowflake.com/en/release-notes/2025/other/2025-11-04-cortex-agents-mcp)

---

## Best for one semantic layer over federated sources: Dremio

Dremio provides a semantic layer with shared definitions for business entities, metrics and relationships, over live access to lakes, warehouses and databases without ETL pipelines or copies. Its MCP server, available Dremio-hosted or self-hosted, lets agents explore data, generate SQL from natural language and create views, so the semantic layer and the agent route are documented together.

**Pros:**
- One semantic layer over federated sources, with no ETL pipelines or copies.
- Snowflake is a documented source, alongside catalogs such as AWS Glue and Unity Catalog.
- Hosted MCP for Claude.ai and ChatGPT; self-hosted MCP for Claude Code, Cursor and other local clients.
- Agents work with Dremio's existing access controls.

**Good to know:**
- Choose the hosted or self-hosted MCP option based on which AI clients your team uses.

Dremio's docs say "the semantic layer provides shared definitions for business entities, metrics, and relationships" and that Dremio Cloud "provides live access to data across lakes, warehouses, and databases without ETL pipelines or copies," with "integration with catalogs such as AWS Glue and Unity Catalog" ([Dremio docs](https://docs.dremio.com/dremio-cloud/about/)). Its MCP server can "explore data, generate SQL queries from natural language, and create views in Dremio," and a tool "retrieves descriptions and tags for a table or schema" ([Dremio docs](https://docs.dremio.com/current/developer/mcp-server/)). Hosted and self-hosted options are documented on the [Dremio MCP server page](https://docs.dremio.com/dremio-cloud/ai-integration/mcp-server/), and Snowflake is a supported source ([Dremio docs](https://docs.dremio.com/dremio-cloud/bring-data/connect/databases/snowflake/)).

| Check | What Dremio documents |
|---|---|
| Meaning layer | AI Semantic Layer |
| Agent access | Dremio MCP server, hosted and self-hosted |
| Cross-vendor scope | Federated sources, including Snowflake, AWS Glue and Unity Catalog |

**Choose Dremio if** your data already sits in several stores and you want one semantic layer over them without an ingestion project.

**Sources:** [Dremio Cloud overview](https://docs.dremio.com/dremio-cloud/about/) / [Dremio MCP server](https://docs.dremio.com/dremio-cloud/ai-integration/mcp-server/) / [MCP server tools](https://docs.dremio.com/current/developer/mcp-server/)

---

## Best for one set of definitions published to several platforms: Atlan

Atlan is the context layer for AI. It sits across the platforms above rather than replacing one, and its Context Engineering Studio builds a context repository once and renders it into the semantic layer each engine expects: a semantic view for Snowflake Cortex Analyst, metric views and a Genie space for Databricks, MetricFlow models for dbt. MCP-capable agents such as Claude read the same repository through the Atlan MCP server.

**Pros:**
- One repository renders into Snowflake semantic views, Databricks metric views and dbt MetricFlow models.
- Snowflake and Databricks deploy directly from Context Engineering Studio, so agents query the warehouse natively.
- Claude, OpenAI, Gemini CLI and other MCP clients read the repository through the Atlan MCP server.
- Context Agents generate and maintain descriptions, metrics and definitions across the estate.

**Good to know:**
- Context Engineering Studio is enabled per tenant in Atlan Labs, with your Atlan account team.
- Each engine still runs its own queries and permissions; Atlan supplies the definitions.

Atlan's docs describe Context Engineering Studio as building a context repository "from the governed context in your Atlan catalog and glossary," then rendering it "as a semantic view for Snowflake Cortex Analyst, metric views for Databricks Genie, MetricFlow models for dbt, and context Claude reads over MCP," with Context Agents that "generate and maintain descriptions, metrics, and definitions across your estate" ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/concepts/what-is-context-engineering-studio)). The Databricks target "publishes the repository as Databricks metric views and a Genie space, so agents query the warehouse natively" ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/references/portability-targets)). Studio is enabled "on your Atlan tenant in Atlan Labs" ([Atlan docs](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/how-tos/get-started)). Across the wider estate, "80+ connectors pull context across your entire data estate," and context reaches tools "through SQL, APIs, and the Atlan MCP server" ([Atlan](https://atlan.com/)).

| Check | What Atlan documents |
|---|---|
| Meaning layer | Context repository: dimensions, facts, relationships and derived metrics, rendered into each engine's semantic format |
| Agent access | Atlan MCP server; native routes of the engines it deploys to; Context Engineering Studio in Atlan Labs |
| Cross-vendor scope | One repository deployed to Snowflake and Databricks and rendered for dbt |

**Choose Atlan if** your agents work across more than one of these platforms and you want one set of governed definitions published to each. The trade-offs are covered in [single-stack lock-in vs a neutral context layer](https://atlan.com/know/ai-agent/context-layer/single-stack-lock-in-vs-neutral-context-layer/), [the multi-cloud context layer](https://atlan.com/know/ai-agent/context-layer/multi-cloud-context-layer/) and [the Atlan context layer as enterprise memory](https://atlan.com/know/atlan-context-layer-enterprise-memory/).

**Sources:** [What is Context Engineering Studio](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/concepts/what-is-context-engineering-studio) / [Portability targets](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/references/portability-targets) / [Atlan MCP overview](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/atlan-mcp-overview)

---

## Best for a GA agent route across AWS services: AWS

AWS gives agents a generally available AWS MCP Server that can call AWS APIs with existing IAM credentials. Business terms live in the Amazon DataZone business glossary, which covers data stored across AWS, on premises and third-party sources, and Amazon Quick Topics act as a semantic layer for the Quick chat agent.

**Pros:**
- AWS MCP Server is generally available.
- One server reaches more than 15,000 AWS API operations with your IAM credentials.
- DataZone catalogs and governs data across AWS, on-premises and third-party sources.
- Quick Topics hold business rules and relationships for the Quick chat agent.

**Good to know:**
- Multi-dataset Topics in Amazon Quick are in public preview.

AWS announced "the general availability of the AWS MCP Server," whose call_aws tool "executes any of the 15,000+ AWS API operations using your existing IAM credentials" ([AWS, 2026](https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/)). DataZone is a service to "catalog, discover, share, and govern data stored across AWS, on premises, and third-party sources" ([AWS docs](https://docs.aws.amazon.com/datazone/latest/userguide/datazone-concepts.html)), and "a business glossary is a collection of business terms (words) that may be associated with assets" ([AWS docs](https://docs.aws.amazon.com/datazone/latest/userguide/create-maintain-business-glossary.html)). In Amazon Quick, "a Topic in Quick is the semantic layer between raw data and business users," and "the Quick chat agent automatically traverses those relationships when answering questions" ([AWS, 2026](https://aws.amazon.com/blogs/machine-learning/build-a-unified-semantic-layer-across-datasets-with-multi-dataset-topics-in-amazon-quick/)). For how DataZone and Glue divide the work, see [AWS DataZone vs Glue Data Catalog](https://atlan.com/know/ai-agent/aws/aws-datazone-vs-glue-data-catalog/) and [the Amazon DataZone data catalog](https://atlan.com/know/ai-agent/aws/amazon-datazone-data-catalog/).

| Check | What AWS documents |
|---|---|
| Meaning layer | DataZone business glossary; Amazon Quick Topics |
| Agent access | AWS MCP Server, GA |
| Cross-vendor scope | DataZone catalogs on-premises and third-party sources |

**Choose AWS if** your agents mainly act on AWS services. For what crosses the AWS boundary, read [AWS Glue and DataZone vs a cross-cloud context layer](https://atlan.com/know/ai-agent/aws/aws-datazone-vs-cross-cloud-data-governance-platform/).

**Sources:** [AWS MCP Server GA](https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/) / [DataZone concepts](https://docs.aws.amazon.com/datazone/latest/userguide/datazone-concepts.html) / [DataZone business glossary](https://docs.aws.amazon.com/datazone/latest/userguide/create-maintain-business-glossary.html)

---

## Best for agent access to Oracle Database: Oracle Autonomous AI Database

Oracle Autonomous AI Database includes a built-in, managed MCP server, released in December 2025, that exposes Select AI Agent tools and custom tools to outside AI applications. Select AI can draw on table and column comments and annotations stored in the database when it generates SQL.

**Pros:**
- Managed, multi-tenant MCP server, so no server to run yourself.
- Available for Autonomous AI Database 26ai and 19c on Serverless Exadata Infrastructure.
- Select AI Agent tools and custom MCP tools for common database operations.
- Select AI uses comments and annotations as metadata when generating SQL.

**Good to know:**
- Oracle announced AI-generated, human-governed business ontologies for Oracle AI Data Platform on October 27, 2026; check availability with Oracle.

Oracle's release notes, dated December 23, 2025, say "Autonomous AI Database now includes a built-in MCP Server that provides a managed, per-database MCP server" ([Oracle release notes](https://docs.oracle.com/en-us/iaas/releasenotes/autonomous-database-serverless/2025-12-mcp-server-for-autonomous-ai-database.htm)). Its documentation describes "a managed, multi-tenant server designed to provide secure, standardized access to database tools and features through the Model Context Protocol" ([Oracle docs](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/mcp-server.html)). AI profiles can "use the table and column comments from the data dictionary to better understand the data" ([Oracle docs](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/create-ai-profile.html)). Oracle AI Data Platform business ontologies "define the concepts, terminology, relationships, and policies used to help analytics, agents, and enterprise applications interpret data consistently" ([Oracle, 2026](https://www.oracle.com/news/announcement/ai-world-oracle-ai-data-platform-brings-ai-assisted-business-context-to-enterprise-ai-2026-10-27/)).

| Check | What Oracle documents |
|---|---|
| Meaning layer | Select AI comments and annotations; business ontologies announced for Oracle AI Data Platform |
| Agent access | Built-in MCP server, released December 23, 2025 |

**Choose Oracle if** Oracle Database holds your system of record and agents need governed access to it.

**Sources:** [MCP server release note](https://docs.oracle.com/en-us/iaas/releasenotes/autonomous-database-serverless/2025-12-mcp-server-for-autonomous-ai-database.htm) / [Autonomous AI Database MCP server](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/mcp-server.html) / [AI profiles](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/create-ai-profile.html)

---

## Best for agents on existing Teradata analytics: Teradata VantageCloud

Teradata offers its MCP server in two forms: the open-source Teradata MCP Server Community Edition, available to Vantage customers since 2025, and Teradata Enterprise MCP, which exposes data, analytics and services as tools with authentication and role-based access control. Teradata also documents logical data modeling, with industry-specific logical data models.

**Pros:**
- Two MCP options: the open-source Community Edition and Enterprise MCP.
- Enterprise MCP adds authentication and role-based access control.
- Industry-specific logical data model frameworks.

**Good to know:**
- Teradata announced Tera Context Engine, a vendor-neutral context layer that is not limited to Teradata-managed data, for availability in Q4 2026.

Teradata announced "the launch of the open-source Teradata MCP Server - Community Edition" and said "Teradata Vantage customers can start leveraging the MCP Server for AI agents today" ([Teradata, 2025](https://www.teradata.com/press-releases/2025/mcp-server-agentic-ai-at-scale)). "Teradata Enterprise MCP exposes enterprise data, analytics, and services as tools that AI agents can use with authentication and role-based access control" ([Teradata](https://www.teradata.com/platform/ai-studio/enterprise-mcp)). Its database design documentation "describes the basics of semantic, or logical, data modeling" ([Teradata docs](https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/Database-Design/Semantic-Data-Modeling)). Tera Context Engine is described as "a vendor-neutral context and orchestration layer that gives AI governed business knowledge," and Teradata says it "will be available in Q4 2026" ([Teradata, 2026](https://www.teradata.com/press-releases/2026/tera-an-agentic-coworker)).

| Check | What Teradata documents |
|---|---|
| Meaning layer | Logical data models; Tera Context Engine announced for Q4 2026 |
| Agent access | Teradata MCP Server Community Edition; Teradata Enterprise MCP |

**Choose Teradata if** your analytics run on Teradata today and you want agents on that data without a migration.

**Sources:** [Community Edition launch](https://www.teradata.com/press-releases/2025/mcp-server-agentic-ai-at-scale) / [Teradata Enterprise MCP](https://www.teradata.com/platform/ai-studio/enterprise-mcp) / [Semantic data modeling](https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/Database-Design/Semantic-Data-Modeling)

---

## Best for governed data products across warehouses: Starburst

Starburst Enterprise includes an integrated MCP server that lets agents run read-only SQL and search and retrieve data products, and Starburst Galaxy offers MCP as well. Starburst describes a data product as a governed dataset bundled with its business definitions, access policies, ownership and lineage, and documents deploying its context layer alongside Snowflake without duplicating data.

**Pros:**
- Integrated MCP server in Starburst Enterprise, plus MCP on Starburst Galaxy.
- Agents can search data products and retrieve their details through MCP tools.
- Data products carry business definitions, access policies, ownership and lineage.
- Works alongside warehouses such as Snowflake without duplicating data.

**Good to know:**
- The MCP query tool runs read-only SQL.

Starburst's docs state that "Starburst Enterprise includes an integrated Model Context Protocol (MCP) server," that "the query tool supports only read-only SQL queries," and that data products are searchable through the searchDataProducts tool ([Starburst docs](https://docs.starburst.io/latest/starburst-ai/mcp-server.html)). Galaxy's MCP server lets AI tools "execute read-only SQL queries against your data through natural language interactions" ([Starburst docs](https://docs.starburst.io/starburst-galaxy/starburst-ai/mcp-server.html)). "A Data Product is a governed dataset bundled with its business definitions, access policies, ownership, and lineage" ([Starburst](https://www.starburst.io/context-layer/)), and Starburst explains how to deploy its context layer "alongside Snowflake without duplicating data" ([Starburst, 2026](https://www.starburst.io/blog/context-engineering-for-snowflake/)).

| Check | What Starburst documents |
|---|---|
| Meaning layer | Data products with business definitions |
| Agent access | Integrated MCP server (Enterprise), MCP on Galaxy; read-only SQL and data product tools |
| Cross-vendor scope | Deploys alongside Snowflake and other sources without duplicating data |

**Choose Starburst if** you want agents to query across warehouses through governed data products.

**Sources:** [Starburst MCP server](https://docs.starburst.io/latest/starburst-ai/mcp-server.html) / [MCP on Starburst Galaxy](https://docs.starburst.io/starburst-galaxy/starburst-ai/mcp-server.html) / [Starburst context layer](https://www.starburst.io/context-layer/)

---

## Best for automated enrichment in IBM Knowledge Catalog estates: IBM watsonx.data

IBM watsonx.data uses the Semantic Automation Layer in IBM Knowledge Catalog to add labels, categories and relationships with AI. The watsonx.data intelligence MCP server gives agents tools across governance, lineage, data products, semantic search and query, and Text-to-SQL, and IBM announced Context in watsonx.data, an open, federated context layer, in private preview.

**Pros:**
- Semantic automation adds labels, categories and relationships to data.
- The MCP server covers Text-to-SQL, semantic search and query, lineage and data products.
- An MCP tool builds glossaries from business documents.
- Ties into IBM Knowledge Catalog governance.

**Good to know:**
- Context in watsonx.data is in private preview.

IBM's docs say "semantic automation for data enrichment is a new feature introduced in IBM watsonx.data," which "leverages Semantic Automation Layer (SAL) in IBM Knowledge Catalog" ([IBM docs](https://www.ibm.com/docs/en/watsonxdata/standard/2.1.x?topic=watsonxdata-semantic-automation-data-enrichment)). The watsonx.data intelligence MCP server lists tool areas including "Semantic search and query" and "Text-to-SQL" ([IBM docs](https://www.ibm.com/docs/en/watsonx/wdi/saas?topic=intelligence-getting-started-watsonxdata-mcp-server)), and its create_glossary_from_files tool "extracts business terms from these documents" ([IBM docs](https://www.ibm.com/docs/en/ws-and-kc?topic=terms-creating-glossaries-mcp)). IBM announced that "Context in watsonx.data (available in private preview) extends watsonx.data with an open, federated context layer" ([IBM, 2026](https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens)).

| Check | What IBM documents |
|---|---|
| Meaning layer | Semantic Automation Layer (labels, categories, relationships); Context in watsonx.data in private preview |
| Agent access | watsonx.data intelligence MCP server, with Text-to-SQL and semantic search tools |

**Choose watsonx.data if** IBM Knowledge Catalog already governs your data and you want enrichment automated.

**Sources:** [Semantic automation](https://www.ibm.com/docs/en/watsonxdata/standard/2.1.x?topic=watsonxdata-semantic-automation-data-enrichment) / [watsonx.data intelligence MCP server](https://www.ibm.com/docs/en/watsonx/wdi/saas?topic=intelligence-getting-started-watsonxdata-mcp-server) / [Glossaries via MCP](https://www.ibm.com/docs/en/ws-and-kc?topic=terms-creating-glossaries-mcp)

---

## Best for agent access to Iceberg on hybrid infrastructure: Cloudera

Cloudera brings agents to Iceberg tables with its MCP Server for Apache Iceberg via Impala, launched in preview in June 2025, and an open-source AI Workbench MCP server for agentic workflows. Its MCP servers run natively inside Cloudera's platform, which suits hybrid and on-premises estates that keep data in place, and Cloudera Data Catalog glossaries hold business terms.

**Pros:**
- MCP servers run natively within Cloudera's platform, avoiding data movement.
- Iceberg-first, a good match for open table formats.
- AI Workbench MCP server is open source.
- Data Catalog glossaries define a hierarchical set of business terms.

**Good to know:**
- Cloudera launched the Iceberg MCP server in preview; check its current status with Cloudera.

Cloudera introduced its MCP Server for Apache Iceberg as "currently available in preview" and says "Cloudera MCP Servers run natively within Cloudera's unified platform, eliminating risky data movement" ([Cloudera, 2025](https://www.cloudera.com/blog/technical/bringing-context-to-genai-with-cloudera-mpc-servers.html)). Its AI Workbench MCP server is "an open-source Model Context Protocol (MCP) server designed to better integrate with your agentic workflow" ([Cloudera, 2025](https://www.cloudera.com/blog/technical/integrate-agentic-workflows-using-cloudera-ai-workbench-mcp-server.html)). "Glossaries enable you to define a hierarchical set of business terms that represents your business domain" ([Cloudera docs](https://docs.cloudera.com/data-catalog/cloud/search/topics/dc-search-assets-glossary.html)).

| Check | What Cloudera documents |
|---|---|
| Meaning layer | Cloudera Data Catalog glossaries |
| Agent access | MCP Server for Apache Iceberg (preview at launch); AI Workbench MCP server, open source |

**Choose Cloudera if** you run Iceberg on hybrid infrastructure and need agents to stay inside your own boundary.

**Sources:** [Cloudera MCP servers](https://www.cloudera.com/blog/technical/bringing-context-to-genai-with-cloudera-mpc-servers.html) / [AI Workbench MCP server](https://www.cloudera.com/blog/technical/integrate-agentic-workflows-using-cloudera-ai-workbench-mcp-server.html) / [Data Catalog glossaries](https://docs.cloudera.com/data-catalog/cloud/search/topics/dc-search-assets-glossary.html)

---

## What are the other ways to give agents shared definitions?

There are two routes beside the 12 above. An agent can call several platforms' MCP servers directly, Databricks for one question and Snowflake for the next, with "revenue" defined on each platform. Or a team can define metrics once in a headless semantic layer: the dbt Semantic Layer lets teams "define metrics on top of existing models" and handles the joins, and AI tools "can connect to the Semantic Layer through the dbt MCP server" ([dbt docs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl)). Other headless layers are compared in [Snowflake semantic views vs Cube vs AtScale](https://atlan.com/know/ai-agent/semantic-layer/snowflake-semantic-views-vs-cube-vs-atscale/). A context layer adds ownership, lineage and certification around those definitions; see [full-stack AI platform vs best-of-breed context layer](https://atlan.com/know/ai-agent/context-layer/full-stack-ai-platform-vs-best-of-breed-context-layer/) and [context layer total cost of ownership](https://atlan.com/know/ai-agent/context-layer/context-layer-tco-build-vs-buy-vs-bundle/). A shared layer earns its keep when agents repeatedly ask questions that span platforms.

---

## How do you choose the right AI-ready data platform?

Choose by where your certified definitions live today and where your agents run, not by a feature list. Start with three questions: which platform already holds your metric definitions, how many platforms an agent needs per question, and who will keep definitions current.

| If your estate looks like... | Start with... | Why |
|---|---|---|
| One platform, no cross-platform joins | That platform's native meaning layer | Native context may be enough; don't overbuy |
| Certified metrics in Unity Catalog | Databricks | Genie One MCP is GA and grounded in metric views |
| Metrics modeled in LookML | Google Cloud | The Looker-managed MCP server is GA and works with LookML models |
| Metrics already modeled in Power BI | Microsoft Fabric | Fabric IQ MCP is GA and runs DAX against semantic models |
| Semantic views in Snowflake | Snowflake | The managed MCP server is GA and applies semantic views through Cortex Analyst |
| Two or more warehouses, data must stay put | Dremio, or a headless semantic layer | One semantic layer over federated sources |
| Agents span several platforms and tools | Atlan | One context repository rendered into each platform's semantic layer |

**One platform:** use what ships with it, and check that the agent route you need is GA and applies the semantic layer.

**Two platforms:** decide where definitions are authored: in each platform, in a federation engine, in a headless semantic layer or in a context layer. The [point solution vs context layer](https://atlan.com/know/ai-agent/semantic-layer/point-solution-vs-context-layer-for-ai-data/) comparison lays out the trade-off.

**Estate-wide agents:** plan for definitions with an owner and a review step. Teams weighing a homegrown route should read [building a DIY context layer](https://atlan.com/know/ai-agent/context-layer/diy-context-layer/) and [how to build MCP servers for enterprise data](https://atlan.com/know/ai-agent/how-to-build-mcp-servers-for-enterprise-data/). For a first pilot, the simpler option is usually right.

---

## Why do AI-ready platforms still give agents wrong answers?

Agents on AI-ready platforms still answer wrongly when the agent route skips the meaning layer, when definitions drift after a schema change, or when meaning covers only part of the question. Platform features solve access; maintaining meaning is still someone's job.

Builders keep filling the gap themselves: Hacker News keeps seeing projects such as [SLayer, a semantic layer maintained by your agent](https://news.ycombinator.com/item?id=48095686) and [Ktx, an executable context layer for data agents](https://news.ycombinator.com/item?id=48309986).

Three mistakes recur:

- **Pointing the agent at the generic route.** Snowflake's docs note that exposing direct SQL on the same MCP server "allows the MCP client to bypass the agent's semantic views," and recommend a separate server for it. See [why text-to-SQL fails without a context layer](https://atlan.com/know/ai-agent/data-for-ai/text-to-sql-for-enterprise/).
- **Modeling meaning in one platform for an estate of three.** The definitions stop where the platform stops.
- **Shipping definitions with no owner.** Schema changes break them silently, and [data quality for AI](https://atlan.com/know/data-for-ai/data-quality-for-ai-agent/) degrades with them.

---

## Real stories from real customers: shared language for AI



      "Atlan captures Workday's shared language to be leveraged by AI via its MCP server. As part of Atlan's AI labs, we're co-building the semantic layer that AI needs."


      - Joe DosSantos, VP, Enterprise Data and Analytics, Workday








      "Atlan is our context operating system to cover every type of context in every system including our operational systems."


      - Sridher Arumugham, Chief Data Analytics Officer, DigiKey






---

## Where should definitions live once agents span platforms?

Four platforms on this list document a semantic layer and a GA agent route that uses it: Databricks, Google Cloud, Microsoft Fabric and Snowflake. Three of them also document definitions over data that other vendors store.

So the platform choice and the meaning choice are separate decisions. Pick the platform for where your data lives. Then decide where definitions are authored and owned: in one platform, in a headless semantic layer, or in a context layer that publishes to all of them. The [enterprise context layer](https://atlan.com/know/what-is-the-enterprise-context-layer/) explains the third option, and [data infrastructure for AI](https://atlan.com/know/data-infrastructure-for-ai/) covers the rest of the stack.

---

## FAQs about AI-ready data platforms

### 1. What is the best AI-ready data platform?

There is no single best platform. On documented capability, Databricks, Google Cloud, Microsoft Fabric and Snowflake rank highest because each pairs a semantic layer with a GA agent route that uses it. The right one is usually the platform that already holds your certified metric definitions.

### 2. What makes a data platform AI-ready?

A data platform is AI-ready when it has a semantic layer that defines measures and joins, an agent route such as an MCP server that applies that layer, and access policies on that route. Storage, compute and vector search alone leave an agent guessing at definitions.

### 3. Which data platforms have an MCP server?

All 11 platforms here document MCP servers or MCP tools, and so does Atlan. Databricks Genie One MCP, the Looker-managed and BigQuery MCP servers, Fabric IQ MCP, the Snowflake-managed MCP server and the AWS MCP Server are GA. Oracle released its built-in MCP server in December 2025, and Cloudera launched its Iceberg MCP server in preview.

### 4. Is Snowflake or Databricks more AI-ready?

On documented capability they are close. Both keep executable meaning natively, in semantic views and metric views, and both have a GA agent route that uses it: Cortex Analyst through Snowflake's managed MCP server, and Genie One MCP. Databricks also documents metric views over foreign tables reached through Lakehouse Federation.

### 5. Do I need a semantic layer if my warehouse has AI features?

Yes, if agents need consistent answers. AI features let an agent query the warehouse, but without a semantic layer it guesses what metrics mean and how tables join. Miro saw more than 65% wrong answers when agents queried over 10,000 tables with no semantic layer to guide them.

### 6. Can one platform's semantic layer describe data in another platform?

Yes, in several ways. Databricks metric views can use foreign tables, Looker connects LookML to Snowflake, Amazon Redshift and Databricks, Fabric references external storage through OneLake shortcuts, and Dremio federates sources such as Snowflake. Atlan renders one context repository into Snowflake and Databricks semantic layers.

### 7. What is the first step to making data AI-ready?

Pick one business question your agents must answer and trace it end to end: the data it needs, the definitions it depends on, and who owns them. That exposes missing meaning faster than a platform evaluation.

### 8. How do I check whether a vendor's agent features are GA?

Read the vendor's current documentation and release notes for an explicit status label. Statuses change often: Google's Looker MCP server went GA on October 26, 2026. Recheck before you commit.

---

## Sources

1. [SQL query logs hold the context AI agents need, VentureBeat](https://venturebeat.com/data/sql-query-logs-hold-the-context-ai-agents-need-to-stop-hallucinating-joins)
2. [Study: AI-ready data platforms 2026, Lufthansa Industry Solutions](https://www.lufthansa-industry-solutions.com/de-en/studies/study-ai-ready-data-platforms-2026)
3. [Making data ready for agentic AI, martinfowler.com](https://martinfowler.com/articles/making-data-ready-for-agentic-ai.html)
4. [Unity Catalog metric views, Microsoft Learn (Azure Databricks)](https://learn.microsoft.com/en-us/azure/databricks/uc-semantics/metric-views/)
5. [Metric view modeling, Microsoft Learn (Azure Databricks)](https://learn.microsoft.com/en-us/azure/databricks/uc-semantics/metric-views/basic-modeling)
6. [Built-in MCP services, Microsoft Learn (Azure Databricks)](https://learn.microsoft.com/en-us/azure/databricks/agents/mcp-tools/built-in-mcp-services)
7. [Genie One, Microsoft Learn (Azure Databricks)](https://learn.microsoft.com/en-us/azure/databricks/genie-one/chat)
8. [Lakehouse Federation, Microsoft Learn (Azure Databricks)](https://learn.microsoft.com/en-us/azure/databricks/query-federation/)
9. [What is LookML, Google Cloud](https://docs.cloud.google.com/looker/docs/what-is-lookml)
10. [Looker release notes, Google Cloud](https://docs.cloud.google.com/looker/docs/release-notes)
11. [Looker MCP server, Google Cloud](https://docs.cloud.google.com/looker/docs/mcp)
12. [Looker dialect support, Google Cloud](https://docs.cloud.google.com/looker/docs/dialects)
13. [BigQuery release notes, Google Cloud](https://docs.cloud.google.com/bigquery/docs/release-notes)
14. [Introduction to BigQuery Omni, Google Cloud](https://docs.cloud.google.com/bigquery/docs/omni-introduction)
15. [Use the BigQuery MCP server, Google Cloud](https://docs.cloud.google.com/bigquery/docs/use-bigquery-mcp)
16. [Fabric IQ overview, Microsoft Learn](https://learn.microsoft.com/en-us/fabric/iq/overview)
17. [Fabric IQ MCP, Microsoft Learn](https://learn.microsoft.com/en-us/fabric/iq/connectors/fabric-iq-mcp)
18. [OneLake shortcuts, Microsoft Learn](https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts)
19. [Overview of semantic views, Snowflake docs](https://docs.snowflake.com/en/user-guide/views-semantic/overview)
20. [Release notes: semantic views generally available, Snowflake](https://docs.snowflake.com/en/release-notes/2025/9_25)
21. [Semantic View Autopilot, Snowflake docs](https://docs.snowflake.com/en/user-guide/views-semantic/autopilot)
22. [Snowflake-managed MCP server, Snowflake docs](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-mcp)
23. [Snowflake-managed MCP server general availability, Snowflake release notes](https://docs.snowflake.com/en/release-notes/2025/other/2025-11-04-cortex-agents-mcp)
24. [Dremio Cloud overview, Dremio docs](https://docs.dremio.com/dremio-cloud/about/)
25. [Dremio MCP server tools, Dremio docs](https://docs.dremio.com/current/developer/mcp-server/)
26. [Dremio MCP server, Dremio docs](https://docs.dremio.com/dremio-cloud/ai-integration/mcp-server/)
27. [Connect to Snowflake, Dremio docs](https://docs.dremio.com/dremio-cloud/bring-data/connect/databases/snowflake/)
28. [What is Context Engineering Studio, Atlan Documentation](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/concepts/what-is-context-engineering-studio)
29. [Context Engineering Studio portability targets, Atlan Documentation](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/references/portability-targets)
30. [Enable Context Engineering Studio, Atlan Documentation](https://docs.atlan.com/product/capabilities/governance/context-engineering-studio/how-tos/get-started)
31. [Atlan](https://atlan.com/)
32. [Atlan MCP overview, Atlan Documentation](https://docs.atlan.com/product/capabilities/atlan-ai/how-tos/atlan-mcp-overview)
33. [The AWS MCP Server is now generally available, AWS](https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/)
34. [Amazon DataZone terminology and concepts, AWS](https://docs.aws.amazon.com/datazone/latest/userguide/datazone-concepts.html)
35. [Create and maintain a business glossary, AWS](https://docs.aws.amazon.com/datazone/latest/userguide/create-maintain-business-glossary.html)
36. [Multi-dataset Topics in Amazon Quick, AWS](https://aws.amazon.com/blogs/machine-learning/build-a-unified-semantic-layer-across-datasets-with-multi-dataset-topics-in-amazon-quick/)
37. [MCP Server for Autonomous AI Database release note, Oracle](https://docs.oracle.com/en-us/iaas/releasenotes/autonomous-database-serverless/2025-12-mcp-server-for-autonomous-ai-database.htm)
38. [Autonomous AI Database MCP server, Oracle docs](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/mcp-server.html)
39. [Create an AI profile, Oracle docs](https://docs.oracle.com/en-us/iaas/autonomous-database-serverless/doc/create-ai-profile.html)
40. [Oracle AI Data Platform brings AI-assisted business context to enterprise AI, Oracle](https://www.oracle.com/news/announcement/ai-world-oracle-ai-data-platform-brings-ai-assisted-business-context-to-enterprise-ai-2026-10-27/)
41. [Teradata MCP Server Community Edition launch, Teradata](https://www.teradata.com/press-releases/2025/mcp-server-agentic-ai-at-scale)
42. [Teradata Enterprise MCP, Teradata](https://www.teradata.com/platform/ai-studio/enterprise-mcp)
43. [Semantic data modeling, Teradata docs](https://docs.teradata.com/r/Enterprise_IntelliFlex_VMware/Database-Design/Semantic-Data-Modeling)
44. [Tera Context Engine announcement, Teradata](https://www.teradata.com/press-releases/2026/tera-an-agentic-coworker)
45. [Starburst MCP server, Starburst docs](https://docs.starburst.io/latest/starburst-ai/mcp-server.html)
46. [MCP on Starburst Galaxy, Starburst docs](https://docs.starburst.io/starburst-galaxy/starburst-ai/mcp-server.html)
47. [Starburst context layer, Starburst](https://www.starburst.io/context-layer/)
48. [Context engineering for Snowflake, Starburst](https://www.starburst.io/blog/context-engineering-for-snowflake/)
49. [Semantic automation for data enrichment, IBM docs](https://www.ibm.com/docs/en/watsonxdata/standard/2.1.x?topic=watsonxdata-semantic-automation-data-enrichment)
50. [watsonx.data intelligence MCP server, IBM docs](https://www.ibm.com/docs/en/watsonx/wdi/saas?topic=intelligence-getting-started-watsonxdata-mcp-server)
51. [Creating glossaries with MCP, IBM docs](https://www.ibm.com/docs/en/ws-and-kc?topic=terms-creating-glossaries-mcp)
52. [Think 2026 announcements, IBM Newsroom](https://newsroom.ibm.com/2026-05-05-think-2026-ibm-delivers-the-blueprint-for-the-ai-operating-model-as-the-ai-divide-widens)
53. [Bringing context to GenAI with Cloudera MCP servers, Cloudera](https://www.cloudera.com/blog/technical/bringing-context-to-genai-with-cloudera-mpc-servers.html)
54. [Cloudera AI Workbench MCP server, Cloudera](https://www.cloudera.com/blog/technical/integrate-agentic-workflows-using-cloudera-ai-workbench-mcp-server.html)
55. [Data Catalog glossaries, Cloudera docs](https://docs.cloudera.com/data-catalog/cloud/search/topics/dc-search-assets-glossary.html)
56. [dbt Semantic Layer, dbt Labs](https://docs.getdbt.com/docs/use-dbt-semantic-layer/dbt-sl)