Trace every AI answer
to the truth.
Every tool in your stack produces its own metadata — schemas, transformations, dashboards, models, policies. None of them pass that context downstream. It leaks at every boundary. Atlan reconstructs it: column-level provenance across 80+ systems, built automatically from your SQL, pipelines, and APIs.
net_revenue since Jan 8 — upstream issue o.amount → net_revenue — checking sourcesamount renamed → net_amount in ORDERS_RAW on Jan 8 — JOIN silently returned nullsamount renamed → net_amount 3 days agorevenue_model JOIN returned nulls silently downstream
ORDERS_RAWCUSTOMERS
revenue_modelREVENUE_AGG
CFO DashboardTrusted by AI-forward enterprises
"In just the first few months, Atlan had lineage across systems like our on-prem Oracle databases, BigQuery data warehouse on Google Cloud, and Looker for visualizations."
Kiran Panja
Managing Director of Cloud Platforms & Engineering, CME Group
"In just the first few months, Atlan had lineage across systems like our on-prem Oracle databases, BigQuery data warehouse on Google Cloud, and Looker for visualizations."
Kiran Panja
Managing Director of Cloud Platforms & Engineering, CME Group
The provenance layer your AI
reads before it acts.
Column-level provenance, reverse-engineered from your entire stack.
ORDERS_RAWDIM_CUSTOMERSREVENUE_AGGA living graph that connects everything and compounds everything.
RAW_SALESREVENUE_AGG"With Google DataPlex, lineage only showed part of the story. Our business operates across many systems and we needed complete, enterprise-wide lineage. Atlan's platform was more intuitive, delivered on complex end-to-end lineage, and had a strong library of connectors. We also used OpenLineage for Spark jobs to tie operational lineage to our data platform."
Zenul Pomal
Core Data Platform & Enterprise Architecture, CME Group
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The leader in lineage across every major report.
Learn more about lineage
with Atlan.
How CME Group and Deutsche Boerse Map Their Data and AI Estate
How two of the world's leading exchanges built end-to-end provenance across their data and AI landscape using Atlan lineage.
Documentation: Lineage on Atlan
Full technical documentation — SQL parsing, API crawling, OpenLineage ingestion, column-level stitching, BI lineage, AI lineage, custom ingestion, and the App Framework APIs.
How CME Group and Deutsche Boerse Map Their Data and AI Estate
How two of the world's leading exchanges built end-to-end provenance across their data and AI landscape using Atlan lineage.
Documentation: Lineage on Atlan
Full technical documentation — SQL parsing, API crawling, OpenLineage ingestion, column-level stitching, BI lineage, AI lineage, custom ingestion, and the App Framework APIs.
Everything you need to know about
data lineage with Atlan
Four methods work together to build one unified graph. SQL parsing reads millions of queries from Snowflake, BigQuery, Redshift, and Databricks to extract transformation logic at the column level. Native integrations crawl each tool's API for pipelines SQL cannot reach. OpenLineage ingestion captures runtime inputs and outputs from Airflow, Spark, dbt Cloud, and Astronomer as they execute. Custom lineage APIs, SDKs, and a visual builder handle anything that falls outside those three. The result is a single, continuously updated lineage graph — automatically reverse-engineered, no manual mapping required.
Through Atlan's MCP Server, AI agents query the lineage graph before they use any data. A single call returns the full context chain for a column: its origin, every transformation it passed through, the quality checks it carries, the governance policies applied to it, and who owns it. The agent knows not just what a column contains — but where it came from, what touched it, and whether it can be trusted before producing an answer.
Every answer an AI agent produces can be traced back through the lineage graph to the data that produced it. The graph shows which columns were queried, what transformations they passed through, what quality checks apply, and what governance policies govern them. This is what AI accountability looks like in practice — not just knowing the answer, but being able to show the full provenance chain behind it, column by column, system by system.
Table-level lineage shows that one dataset feeds another. Column-level lineage shows exactly which fields flow between them — and what happens to them in transit. That distinction matters for AI: an agent working with a revenue metric needs to know whether that column traces to gross or net revenue, which transformations modified it, and whether a quality issue upstream is currently affecting it. Column-level granularity is what makes AI answers traceable and trustworthy.
Tag a column as PII once — lineage propagates that classification to every downstream asset automatically and syncs bi-directionally with Snowflake and Databricks. When a quality check fails upstream, the lineage graph surfaces every downstream dashboard, pipeline, and AI agent affected. AI agents reading from the graph inherit these classifications without additional configuration. Context and policy compound as they travel the graph — the more connected your lineage, the smarter your governance.
Before a data engineer ships a change, Atlan calculates the full blast radius and surfaces it inside the GitHub or GitLab pull request — listing every downstream dashboard, pipeline, AI agent, and data product that would be affected. Teams see the impact before the change lands, not after a pipeline breaks or an AI agent starts returning wrong answers.
Every system in your stack creates context.
Only lineage connects it.
