70 docs tagged with "governance"

Learn how to manage user permissions and access to data assets in Atlan for security and compliance.

Export administrative data into Excel — users, groups, personas, purposes, and policies for backup, audits, and offline access model analysis.

Establish robust AI governance frameworks that maintain compliance, mitigate risks, and drive trust through visibility, lifecycle management, and policy enforcement

Configure AI asset access in personas — control who can view, edit, or manage AI models, model versions, applications, and governance properties.

Migrate glossary terms, tags, and metadata from Alation to Atlan—configure asset mapping, image handling, and custom metadata preservation.

Automatically link glossary terms to matching assets — map terms by name patterns, asset types, and custom filters.

Bulk import glossary terms and assets from CSV files — configure term hierarchy, relationships, and business metadata.

Bulk import tag definitions into Atlan from a CSV file. Reference for workflow configuration, CSV format, tag properties, and asset-storage options.

Use the Asset-term link app to automatically create and manage links between glossary terms and matching assets based on names and filters.

Learn how to automatically provision Atlan groups based on user designations at first login using the designation-based group provisioning app.

Learn how to automatically assign roles and sub-roles to users in Atlan based on their group memberships using the group-role sync app.

Set up and configure BigQuery for data quality monitoring through Atlan.

Learn how to create and maintain a centralized business glossary in Atlan to standardize terminology and definitions across your organization.

Understand how Data Quality Studio uses compute resources, how costs are calculated, and practical ways to track and optimize spend for Snowflake, Databricks, and BigQuery.

Automate metadata enrichment at scale using AI-powered context agents that generate descriptions, READMEs, and SQL intelligence across your most important data assets.

Learn how to manage data contracts and agreements in Atlan to ensure data quality and compliance.

Learn how to create an AI app in Atlan.

Learn how to create an AI model in Atlan.

Use the Metadata Policy Helper app to create metadata policies in personas using advanced asset selection patterns. Setup requires the app access and connection admin rights.

Understand how credits are consumed by context agents in Context Agents Studio and how to track usage in the Reporting section.

Learn how to create and manage custom metadata attributes in Atlan to extend your data catalog with organization-specific information.

Expand existing custom metadata definitions to include new connections or glossaries added after initial creation. Reference for workflow configuration and scope-extension rules.

Complete reference for custom metadata property configuration, including all field types, settings, and asset targeting options.

Create and manage data products to organize and govern your data assets by domain.

Monitor and maintain data quality across your data sources with automated quality checks, alerts, and governance workflows

Set up and configure Databricks for data quality monitoring through Atlan.

Automatically provision Atlan groups based on user designations — maps user roles to groups at first login.

Link AWS SageMaker Unified Studio assets to Atlan Data Domains for unified governance.

Learn how to organize and manage domains in Atlan to structure your data assets in a logical and business-aligned way.

Step-by-step guide to triggering AI-powered metadata enrichment using context agents in Context Agents Studio.

Understand how context agents process knowledge files to extract business rules, create glossary terms, and generate skill files

Reference documentation for the GLOSSARY_DETAILS table containing glossary, category, and term details

Keep metadata current, enrich quality alerts with lineage context, propagate PII tags through lineage, and score assets for AI readiness using Atlan MCP.

Build a golden dataset of unstructured business content in Atlan—SOPs, policies, and compliance rules—as governed, discoverable assets that context agents use for enrichment and skill generation

Understand knowledge folders and knowledge files as catalog asset types in Atlan.

Use Lakehouse to run advanced lineage analysis across systems and connectors.

Export complete lineage information from your metadata lakehouse for reuse across impact, dashboard, and root cause analysis.

Use Lakehouse to find all downstream dashboards impacted by upstream data changes.

Use Lakehouse to understand downstream impact before making changes to data assets.

Use Lakehouse to trace upstream dependencies and find the source of data issues.

Use Lakehouse to measure and improve tag coverage and propagation across systems.

Use Lakehouse to track metadata enrichment coverage across assets and systems.

Answers to common questions about Context Agents Studio—covering enrichment behavior, collections, agent support, processing time, and AI credit usage.

Use Lakehouse to export rich metadata for AI applications, data marketplaces, and syncing context back to source systems.

Configure metadata access in personas — control who can view, edit, or restrict metadata, tags, terms, and governance properties.

Configure metadata policies at scale using policy templates — automate permission assignment across assets, taxonomies, and custom metadata.

Propagate tags or custom metadata from child assets to parent assets using configurable priority rules. Reference for workflow configuration, priority-rule syntax, and transformation settings.

Monitor governance metrics in the reporting center dashboard. Track query access for personas and purposes, view tag propagation across assets, and manage governance requests from a centralized view.

Request and manage changes to assets that you don't have direct edit access to.

Configure BigQuery to enable data quality monitoring through Atlan.

Configure Databricks to enable data quality monitoring through Atlan.

Configure Snowflake to enable data quality monitoring through Atlan.

Set up and configure Snowflake for data quality monitoring through Atlan.

Automatically populate asset owners in Atlan using source-system metadata like Snowflake last_ddl_by and Tableau source_owner. Reference for workflow configuration and owner-mapping rules.

Learn how to implement data stewardship in Atlan through automated workflows, policies, and task management.

Use the reporting center to track asset enrichment, monitor metadata updates, and review governance metrics. Access dashboards for asset metrics, glossaries, governance, queries, automations, and usage and cost tracking.

Sync AWS Lake Formation tags to Atlan custom metadata to maintain governance alignment. Reference for workflow configuration, tag mapping, and input-file format specifications.

Learn how to use tags in Atlan to categorize and organize your data assets for improved discoverability and governance.

Resolve common issues with Confluence Integration Manager credentials and authentication.

Understand the collections available in Context Agents Studio—curated groups of data assets automatically surfaced from usage signals to help you prioritize metadata enrichment.

Learn about the AI-powered context agents available in Context Agents Studio—specialized agents that generate descriptions, READMEs, and SQL intelligence.

Upgrade your Snowflake data quality setup to the latest version

Let Atlan suggest data quality rules automatically based on your asset's metadata structure, and apply them in a few clicks.

Automatically downgrade offboarded users to Guest role — configure workflows for secure access revocation when users leave.

Import user roles from external sources into Atlan — automate role assignment based on user attributes and systems of record.

Complete configuration reference for the User Role Sync app properties and settings.

Learn how to manage users and groups in Atlan to control access and organize your data team.

View audit logs to track configuration changes made to your workflow connections.

Atlan MCP use cases across chat-based AI tools (Claude, Cursor, ChatGPT, Gemini), automation platforms (Python, n8n, LangChain), and end-to-end workflows for metadata enrichment, governance, data engineering, asset lifecycle, and catalog adoption.

Understand Atlan's Data Quality Studio and how it enables business and data teams to collaborate on defining, monitoring, and enforcing data quality expectations