The single source of
truth for humans and AI
agents
The single
source of truth
for humans and
AI agents
Empower humans and AI-agents to find, understand and trust your data with Atlan's enterprise data catalog. Drive adoption with AI-assisted documentation and discovery.
Trusted by companies with more than $10T in enterprise value
Google for your data
Natural language search
Get results for synonyms related to your keyword.
Search through business context
Business users can find assets linked to business metrics.
Search using SQL syntax
Data engineers can discover data through 'db.schema'.
Search across your
entire data asset universe
Browse like you're shopping for data
Create filters with any metadata property. Analyst? Engineer? Architect? Get your personalized data shopping experience.
Find trust signals
in our Companion Sidebar
Trust signals are the missing clues you need to discovering data. Did your analyst use this table last week? Does it power an executive dashboard downstream? Is the table verified by your engineer?
Atlan's Companion Sidebar uncovers trust signals for every asset — from table to term — so you can back your data choices.
Industry leaders work with Atlan
to unlock value from data & AI
Discovery & Catalog
"Everyone keeps saying it's like having Google for our data. Starting with column names and descriptions helps users understand what's in our lakes and warehouses—and ask the right questions."
Data Discovery
"Atlan started getting us this immediate value through data discovery. The way I keep thinking about it was the early days of the internet. You had access to this vast amount of information, but it was really hard to find. Then, search engines made it easy to run a search and find relevant content. For us, Atlan became Google for Data."
Frequently Asked Questions:
Data Discovery & Catalog
What is a data catalog?
A data catalog is a centralized inventory of data assets that uses metadata to help users discover, understand, and trust data across systems. Atlan transforms catalogs from static inventories into active metadata platforms serving both human users and AI agents. It continuously captures technical metadata (lineage, usage, quality) and enables business context enrichment (definitions, owners, certifications). This serves diverse consumers—analysts understanding metrics, engineers finding datasets, business users discovering reports, and AI systems querying metadata for context.
What's the difference between a data catalog and data dictionary?
A data dictionary documents technical specifications for a specific database—table structures, column definitions, data types, and constraints. A data catalog provides organization-wide visibility across all data systems, functioning as a unified context and control plane for technical, governance, operational, and business metadata. Atlan integrates dictionary capabilities within the catalog, so column-level documentation connects to assets across your entire ecosystem rather than existing in database-specific silos.
Data catalog vs. data discovery – what's the difference?
A data catalog organizes metadata about your data assets. Data discovery is finding and exploring data to answer specific questions. Atlan collapses this distinction through AI-powered search that uses governed metadata context. Users go from question to insight in seconds—finding definitions, owners, lineage, and dashboards without knowing where data lives. Active metadata continuously enriches discovery as usage patterns change, making search results contextual rather than keyword-based.
How does a data catalog support AI and analytics?
Data catalogs provide the context layer AI systems need: discoverable training data, documented lineage for explainability, governance controls for compliance, and quality validation for reliability. Atlan's Metadata Lakehouse functions as a unified context engine across data and AI systems. Column-level lineage tracks features from raw inputs through transformations to models. AI Governance Studio auto-discovers models and classifies them against frameworks like the EU AI Act, ensuring AI systems operate on trusted, governed data.
What is data lineage?
Data lineage tracks how data flows from source through transformations to destination, documenting origin, changes, and dependencies across systems. Atlan captures end-to-end, column-level lineage automatically—tracking individual fields through transformations without manual documentation. This granularity enables debugging (trace bad data to source), impact analysis (assess change effects before deployment), and compliance (prove data handling for regulations). Atlan scales lineage to millions of assets with consistent performance.
How long does it take to implement a data catalog?
Modern catalogs like Atlan deliver value within weeks. Legacy enterprise catalogs often require 6-12+ months for deployment. Atlan's architecture with 100+ pre-built connectors and automated metadata discovery enables rapid implementation. Technical metadata (lineage, schemas, usage) becomes available as soon as connectors run, providing immediate value for technical users. Teams then iteratively enrich business context. Most customers reach full production deployment in 4-8 weeks.