Connect Amazon Athena to Atlan's Lakehouse using AWS Glue Catalog Federation. Setup registers Atlan's Iceberg REST Catalog as a federated data source in Lake Formation, making Lakehouse tables available to Athena and other AWS query engines.
Connect BigQuery to Atlan's Lakehouse to query catalog metadata from BigQuery SQL. Setup requires a BigQuery connection (Cloud Resource for GCS-backed tenants or BigLake on AWS for S3-backed tenants), a one-time storage access grant from Atlan Support, and a Python script that creates the external tables.
Connect Confluence to Atlan through Atlassian's Rovo MCP server so agents can read spaces and pages as context.
Connect your own MCP server to Atlan so agents can read the knowledge it serves, for sources Atlan doesn't connect to natively.
Connect Databricks to Atlan's Lakehouse using foreign Iceberg tables in Unity Catalog to query metadata. Setup creates storage credentials and an external location pointing to Atlan's Lakehouse data in your cloud storage.
Connect GitHub to Atlan so agents can read repository READMEs, docs, and markdown files as context.
Query your Atlan Metadata Lakehouse tables directly in Microsoft Fabric with zero copy. An administrator connects a Fabric workspace and Atlan creates a OneLake shortcut for each Iceberg table, making your metadata queryable across Power BI, notebooks, and SQL with no data movement.
Connect PySpark to Atlan's Lakehouse through the Iceberg REST catalog with Polaris credential vending. Credential vending automatically issues short-lived cloud storage credentials for each request without requiring hardcoded storage access keys.
Get your Lakehouse connection details and connect your preferred Iceberg REST–compatible client
Connect SharePoint to Atlan through OneLake shortcuts or Microsoft Graph so agents can read your document libraries as context.
Connect Snowflake to Atlan's Lakehouse using Snowflake's catalog-linked database feature to query metadata in standard SQL. Setup creates a catalog integration that links Atlan's Iceberg REST Catalog to Snowflake.
Step-by-step guide to creating a context agent from a template in Atlan, from picking the template through to your first preview run.
Describe your use case in Context Engineering Studio, approve the assets CES proposes, and review the semantic model it assembles. The repository references your Atlan assets instead of copying them.
Learn how to deploy the Atlan browser extension across your organization using managed browsers and MDM software on macOS and Windows.
Deploy a context repository in Context Engineering Studio to Databricks Genie. CES pushes descriptions into Unity Catalog, creates one Metric View per table, and configures a Genie Space.
Deploy a context repository from Context Engineering Studio to Snowflake Cortex Analyst as a Semantic View, then grant access to your business users and verify the view in Snowflake.
Enable ML-based anomaly detection on a Snowflake table to automatically monitor row count and freshness without manual thresholds.
Learn how to enable embedded Atlan metadata in Tableau dashboards for seamless data context and governance.
Step-by-step guide to triggering AI-powered metadata enrichment using context agents in Context Agents Studio.
Export a filtered set of certified assets from Atlan as a CSV file and deliver it directly to email recipients. The Asset export (advanced) app lets you share governance summaries and asset inventories with stakeholders who don't have direct Atlan access.
Get your Lakehouse connection details from Atlan and configure any Iceberg REST–compatible client to start querying your metadata. Setup requires credentials from the Lakehouse Marketplace view in Atlan.
Grant the Unity Catalog and workspace permissions Context Engineering Studio needs to deploy a context repository to Databricks Genie. Required at deploy time. You can build the repository without them.
Grant the Snowflake permissions Context Engineering Studio needs to deploy a context repository to Snowflake Cortex Analyst. Required only at deploy time. You can connect and build without them.
Build your first context layer for one domain, one team, and one AI client using Atlan connectors, context agents, and the Atlan MCP server.
Build an enterprise context layer for AI agents across multiple domains, governed for compliance, using Atlan Context Engineering Studio and the Atlan MCP server.
Add data-side observability to production AI agents with Atlan, covering lineage, access enforcement, and audit logs. Pair with an LLM observability tool.
Build long-term memory for AI agents with Atlan context repositories and the Atlan MCP server. Covers prerequisites, MCP wiring, and validation.
Write an AGENTS.md file for AI coding agents (Codex, Cursor, Copilot, Aider, Windsurf, Zed). Spec, examples, required and optional sections, and pitfalls.
Bulk import or update asset metadata in Atlan using a CSV file stored in Amazon S3. The Asset Import app supports direct CSV import from S3 buckets, enabling you to create or update connections, databases, schemas, tables, and columns in one workflow.
Step-by-step guide to importing glossary terms, categories, and glossaries into Atlan using a local CSV file.
Learn how to install and configure the Atlan browser extension in Chrome or Edge, connect it to your Atlan workspace, and set up custom domains for your data tools.
Put a collection on a schedule so its AI-written descriptions and READMEs are filled in for new assets and regenerated when an asset changes.
Step-by-step guide to creating an Asset ↔ Term Linker agent in Atlan, running a sample, reviewing the proposed term links, and publishing them.
Learn how to run a context agent, read its output, change its settings, put it on a daily, weekly, or monthly schedule, and delete it.
Export assets, glossaries, and data products out of Atlan to a file—as a download, by email, or to object storage—using the Python SDK.
Build lineage across multiple connections of a single connector using the Python SDK, and re-scope the workflow as connections are onboarded.
Create helper lineage tables in your warehouse for advanced lineage queries that include direction (upstream/downstream), hop level, and asset names. These tables work with Lakehouse's native LINEAGE_ADJACENCY_LIST table.
Learn how to use the Atlan browser extension to access metadata in-flow inside your data tools, search Atlan from any webpage, and link web pages as resources to assets.
Point an Atlan agent at the knowledge files already uploaded to your workspace, with no connector and no credentials to set up.
Learn how to write custom instructions that help Context Agents Studio generate accurate, business-relevant metadata descriptions for your organization.
Learn how to write run instructions that tell a context agent template exactly which terms to extract from your documentation.