---
title: "Data Lineage on the Context Layer | Trace Every AI Answer | Atlan"
url: "https://atlan.com/data-lineage/"
description: "Column-level data lineage across 80+ systems, reverse-engineered automatically from SQL, pipelines, and APIs. Built for the era when AI is the one answering."
keywords: "data lineage, column-level lineage, data provenance, AI lineage, data governance, atlan"
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

**Data Lineage: 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. The hero shows the Atlan AI Lineage Agent answering a question like "why did the CFO dashboard revenue drop this week?" by tracing lineage upstream to the root cause and listing the downstream assets at risk.

[Book a Demo](https://atlan.com/forms/talk-to-sales-contact/)

## Trusted 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

## The provenance layer your AI reads before it acts

### How Atlan builds lineage - column-level provenance, reverse-engineered from your entire stack

Four methods. One unified graph.

- **SQL parsing.** Atlan's parser reads millions of SQL queries from data systems like Snowflake, BigQuery, Redshift, and Databricks to extract the actual transformation logic.
- **Native integrations.** For the tools that SQL parsing can't reach, Atlan crawls each tool's native API to extract field-level dependencies, then stitches them into the same lineage graph.
- **OpenLineage.** Atlan natively consumes OpenLineage events from Airflow, Spark, dbt Cloud, and Astronomer, capturing runtime inputs, outputs, and column-level transformations as pipelines execute.
- **Custom lineage.** Atlan's App Framework provides REST APIs and SDKs for programmatic lineage, CSV imports for bulk ingestion, and a visual Lineage Builder for manual modeling.

### Context compounding - a living graph that connects everything and compounds everything

- **Quality compounds along lineage.** When a quality check fails upstream, lineage shows every downstream dashboard, pipeline, and AI agent affected. Root cause analysis goes from days to minutes.
- **Governance propagates along lineage.** Tag a column as PII once; lineage propagates that classification to every downstream asset and syncs bi-directionally with Snowflake and Databricks.
- **Impact analysis travels along lineage.** Before a data engineer ships a change, Atlan shows the full blast radius inside the GitHub or GitLab pull request: downstream dashboards, pipelines, AI agents, data products.
- **AI agents read the full context chain.** Through Atlan's MCP Server, AI agents query the lineage graph before they use data. An agent checking a column gets back provenance, quality score, governance policy, and ownership, in one call.

## Customer story: CME Group

"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

CME Group stats: **18M+** assets cataloged; **1,300+** glossary terms connected; **100+** active users. [Watch the case study](https://atlan.com/regovern-watch-center/cme-group-evaluation/)

## Industry recognition: the leader in lineage across every major report

- **Leader in the 2025 Gartner Magic Quadrant for Metadata Management Solutions:** "Atlan scored highest for data lineage and impact analysis. Customer references praise the ease of use of the technology, and particularly the lineage and search capabilities." [Report](https://atlan.com/gartner-magic-quadrant-metadata-management-solutions-2025/)
- **Leader in the 2026 Gartner Magic Quadrant for Data & Analytics Governance:** "The core architecture of Atlan's 'metadata lakehouse' is built on Apache Iceberg's open table format, which allows for scalable, unified metadata cataloging for structured, semistructured and unstructured data." [Report](https://atlan.com/gartner-magic-quadrant-data-governance-2026/)
- **Leader in The Forrester Wave: Data Governance Solutions, Q3 2025:** "Users consistently praise Atlan's ease of use, flexibility, and responsiveness. They highlight its intuitive UI, strong lineage and smart search capabilities, and seamless integration across the data stack." [Report](https://atlan.com/know/forrester-wave-data-governance-2025/)

## Learn more about lineage with Atlan

- [How CME Group and Deutsche Boerse map their data and AI estate](https://atlan.com/regovern-watch-center/) - how two of the world's leading exchanges built end-to-end provenance across their data and AI landscape using Atlan lineage (recording).
- [Documentation: Lineage on Atlan](https://ask.atlan.com/hc/en-us/categories/4414135917457-Lineage) - SQL parsing, API crawling, OpenLineage ingestion, column-level stitching, BI lineage, AI lineage, custom ingestion, and the App Framework APIs.

## FAQ

**How does Atlan build lineage across the enterprise data estate?**
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.

**How do AI agents query Atlan's lineage graph?**
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 where a column came from, what touched it, and whether it can be trusted before producing an answer.

**How does Atlan help trace AI answers back to source data?**
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: the full provenance chain behind an answer, column by column, system by system.

**What is the difference between table-level and column-level lineage?**
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. 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.

**How does governance propagate along the lineage graph?**
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. The more connected your lineage, the smarter your governance.

**How does impact analysis work before a schema change?**
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. [Book a Demo](https://atlan.com/forms/talk-to-sales-contact/)