Atlan Data Catalog & Governance

Atlan is a data catalog and governance platform that unifies metadata from various sources—databases, data lakes, BI tools, and data pipelines—into a single repository. Designed for cross-functional teams, it offers Google-style searches based on natural language, business context, or SQL syntax, so analysts, engineers, and business users can quickly locate the information assets they need.

Its standout features include automatic construction of a business glossary, column-level lineage that tracks the journey of data from its source to dashboards, and active governance through policies based on roles and sensitivity labels. In addition, Atlan incorporates continuous metadata enrichment, collaboration panels embedded in tools like Slack or Jira, and a layer of open APIs to integrate with the existing data ecosystem.

As strengths, it provides an “active metadata” model that automates documentation and classification tasks, fosters collaboration across teams, and accelerates adoption thanks to its intuitive interface. Among its weaknesses, some customers report limitations in customizing user interface components and an initial learning curve to master all its advanced capabilities.

Main Features of Atlan Data Catalog & Governance

1. Unified Discovery and Catalog

Atlan centralizes metadata from multiple sources—data warehouses, lakes, BI tools, and pipelines—into a single repository. Each asset receives a rich profile with descriptions, schemas, owners, and quality metrics, so any user can find what they need in seconds. The platform includes customizable filters and facets, searches with autocomplete, and contextual suggestions, avoiding endless navigation through folders or information silos. Thanks to scheduled crawls and change-triggered updates, the catalog always reflects the most recent state of the data without manual intervention.

2. Active Business Glossary

Atlan’s business glossary is not a simple static dictionary, but a living space where terms, metrics, and taxonomies specific to the organization are defined. Each term is automatically linked with data assets and dashboards, inserting definitions directly into analytics interfaces such as Looker, Tableau, or Power BI. Versions and change history are recorded, ensuring traceability of who modified each concept and when. This unifies language between technical and business teams, reducing misunderstandings and accelerating catalog adoption.

3. Column-Level Data Lineage

Atlan automatically maps the flow of data from its origin to final consumption, including transformations at the column level. Through standard connectors (Snowflake, BigQuery, dbt, Airflow, etc.), it extracts dependencies and generates interactive lineage graphs. This facilitates impact analysis in the face of schema changes and detects bottlenecks in pipelines. Users can navigate lineage branches, filter by type of transformation, and export views for audits or compliance.

4. 360° Profiles of Data Assets

Every table, view, or dataset has a “360° profile” that combines technical metadata, quality metrics, query history, and collaborative annotations. From a single screen you get a masked preview of the data, links to documents (READMEs), Jira tickets or Slack channels, and comments from teammates. Associated dashboards are embedded, allowing a one-click jump from a visualization to its source. This integrated view speeds up decision-making by providing all the context in one place.

5. Active Governance and Compliance

Atlan implements granular access policies based on roles, groups, or sensitivity labels (PII, confidential, internal). The policy engine is applied at each connection, table, and even column, blocking unauthorized queries and encrypting data as required. Compliance rules (GDPR, HIPAA) are automated with AI classifiers that detect sensitive data and assign controls. It also generates compliance reports and records an access history for audits.

6. Integrated Collaboration

The platform incorporates native collaborative workflows and connects bidirectionally with tools like Slack, Microsoft Teams, and Jira. Users can start discussions, assign governance tasks, request permissions, or certify assets without leaving their preferred channel. Notifications are managed from Atlan and can include metadata context, direct links to profiles, and message templates. This promotes shared accountability and speeds incident resolution.

7. Search with Natural Language and SQL

Atlan offers a “Google for your data” search experience, accepting queries in natural language and SQL syntax. The system interprets business terms and maps them to columns, tables, or glossary definitions, returning results ranked by relevance and confidence. For advanced SQL users, autocomplete suggests code snippets, variables, and joins based on metadata. Searches can be saved and shared in collections, facilitating query reuse.

8. Extensibility via Open APIs

All Atlan functionality is available through REST APIs and SDKs in Python and Java. This enables integrating ingestion, tagging, or lineage flows into CI/CD pipelines, automating data quality playbooks, and developing complementary applications. The APIs cover asset management, glossaries, policies, and workflows, enabling large-scale orchestrations. Organizations can thus incorporate Atlan into event-driven architectures, trigger real-time updates, and tailor the catalog to their internal processes.

9. Domain and Data Product Modeling

Atlan introduces domain and product concepts to organize assets by business area (marketing, finance, sales). Within each domain, data products group relevant tables, pipelines, and dashboards. Products include health metrics (“product score”), business criticality, and sensitivity levels. This abstraction helps delegate responsibilities to specialized teams, monitor adoption, and measure the value generated by each dataset.

Technical Review of Atlan Data Catalog & Governance

Atlan is an enterprise solution focused on metadata management and data governance, designed to provide a unified view of all information assets within the organization. Its API-first architecture ensures smooth integration with more than 100 sources, from data warehouses and BI tools to ETL pipelines. The platform adopts an active metadata approach that keeps the catalog constantly up to date without manual intervention and facilitates regulatory compliance in changing environments.

Automatic metadata ingestion is key to eliminating repetitive tasks: Atlan detects changes in schemas, tables, and dashboards and reflects them instantly in its central repository. Thanks to its native connectors, the platform captures both structural information and real-time usage and quality metrics, boosting operational visibility and continuous governance.

The semantic search layer makes it possible to find assets through queries in natural language or SQL, supported by a system of dynamic facets that adapts filters to the context of each search. This feature drastically reduces the time spent locating data and mitigates the risk of duplicated effort, especially in organizations with large volumes of distributed assets.

Each cataloged item has a 360° profile where business descriptions, data samples, query history, quality metrics (completeness, uniqueness, distribution), and links to external documentation (READMEs, Slack threads, Jira tickets) are consolidated. This holistic view streamlines decision-making and enables quick evaluation of any asset’s suitability for specific projects.

The governance module includes a glossary of terms with hierarchies and synonyms, as well as RBAC and ABAC policies applicable at the catalog, database, table, or column level. You can define masking rules and access restrictions in accordance with regulations such as GDPR or CCPA, all synchronized with identity providers (Okta, Active Directory).

Data lineage provides a complete visual map of upstream and downstream flows, highlighting dependencies and critical points. This characterization facilitates audits and impact analyses before applying changes, reducing the likelihood of disruptions in production processes.

The collaboration space integrates comments, ratings, and notifications on assets, enabling the assignment of stewards and the request of permissions directly in the interface. By connecting Slack and Jira conversations, Atlan fosters shared responsibility and accelerates workflows among engineers, analysts, and business stakeholders.

Finally, the platform offers a Python SDK, webhooks, and RESTful APIs to automate tasks, generate reports, and develop custom connectors. This extensibility ensures that Atlan adapts to heterogeneous architectures and evolves alongside each organization’s needs.

Licensing and Installation of Atlan

Atlan is distributed under a subscription license, structured into Starter, Premier, and Enterprise plans with variable metrics for users, connectors, and technical support; it is aimed primarily at medium and large enterprises that require advanced governance and data lineage; its deployment model favors a SaaS rollout with the option to configure private cloud environments or managed services according to security and compliance policies2.

References

Official Atlan page: Atlan Active Metadata Platform