Lineage reads your warehouse, your dbt models, and your BI layer, then draws the path every column travels — from raw source to the number on the dashboard. So the next time someone asks "where does this metric come from" or "what breaks if I drop this column," the answer is a graph you can click through, not a Slack thread that dies unanswered.
Mapping the warehouses behind data teams that ship daily
A data catalog that only lists tables is a phone book with no map. Lineage parses the SQL itself, so the catalog doesn't just tell you a column exists — it shows you where it was born, every transform it passed through, and everything downstream that depends on it.
Lineage reads the actual queries behind your models and views and resolves the path of every individual column — including joins, CTEs, window functions, and case statements. You see that revenue_usd is built from stripe.charges.amount through four transforms, not just that two tables are vaguely related.
Before you drop a column or rename a model, Lineage shows you everything downstream that would break — every dbt model, dashboard, and reverse-ETL sync that touches it. Ship the change knowing the blast radius, instead of finding out when the CFO's report goes blank.
Type a metric, a column, or a half-remembered table name and find it across every schema, with its definition, owner, freshness, and lineage attached. The answer to "do we already have this number" takes seconds, so analysts stop rebuilding tables that already exist.
Lineage harvests descriptions from dbt, column comments, and the queries themselves, then keeps them in sync as the warehouse changes. Documentation stops being a quarter-old wiki nobody trusts and becomes a live reflection of what's actually running.
Certify the tables your team should build on, flag the ones being deprecated, and surface freshness and test status right in the catalog. People learn which numbers to trust at a glance, instead of guessing between three tables that all claim to hold revenue.
Lineage reads query history to show which tables and columns actually get used and by whom. The asset 200 queries depend on rises to the top; the abandoned copy nobody has touched in a year stops masquerading as a source of truth.
What changes when lineage is a graph, not a guess
Lineage is read-only by design. It connects to the systems you already run, parses query logs and schemas to build the map, and works entirely with metadata — your rows never leave your environment.
Native connectors for Snowflake, BigQuery, Databricks, Redshift, and Postgres, plus deep dbt, Looker, Tableau, and Fivetran integrations. Point Lineage at a warehouse and the graph builds itself from your existing query history.
Lineage parses schemas, query logs, and model definitions to draw the map. It never reads, copies, or stores the contents of your tables — the actual data stays exactly where it lives.
A GitHub check posts the downstream impact of every pull request, a Slack app answers lineage questions inline, and a typed API and webhooks wire the catalog into the rest of your platform.
The graph refreshes as your warehouse changes — new models, renamed columns, dropped tables — so the catalog reflects production today, not the day someone last ran the crawler.
The hard questions in a data team rarely have a fast answer. Lineage turns each one into a click-through on the same map of how your data actually flows.
See every downstream model, dashboard, and sync that depends on a column before you touch it, so a refactor stops being a leap of faith.
Click any metric and walk its lineage all the way back to the raw source row, resolving disputes about whose revenue figure is right in minutes.
When a number looks off, trace upstream to the model or source that broke and find the failed load or bad transform without grepping a dozen logs.
Search every schema for a metric before building it, so analysts reuse the certified table instead of spinning up the fourth copy of orders.
Every asset carries its owner, freshness, and certification status, so questions land with the right person instead of bouncing around #data-help.
Find the stale tables nothing queries and the ones half the company depends on, and retire dead assets with confidence instead of fear.
“A renamed column used to mean a week of broken dashboards we discovered one angry Slack message at a time. Now the GitHub check tells us exactly what a change breaks before we merge. Our analytics refactors went from terrifying to routine.”
“Three teams each had a 'revenue' table and none of them agreed. We traced all three back through Lineage, found the one transform where they diverged, and finally have a single number the whole company trusts. That alone paid for it.”
“When a board metric looks wrong, I used to lose a day to it. Last week I clicked the number, walked the lineage upstream, found the failed Fivetran load in two minutes, and was back to work. It changed what a data incident costs us.”
Connect one warehouse and map it free. Every plan includes full column-level lineage — the map is the product, never the upsell.
For teams getting their warehouse in order.
For data teams shipping changes every day.
For platform teams governing many warehouses at once.
No. Lineage works entirely with metadata — schemas, query logs, and model definitions. It parses the SQL to build the lineage graph but never reads, copies, or stores the contents of your rows. Your actual data stays in your warehouse.
Table-level lineage tells you table A feeds table B. Column-level lineage tells you that revenue_usd in B is built specifically from amount_cents in A, through the exact transforms in between. That precision is what lets impact analysis tell you which columns break, not just which tables are vaguely connected.
Lineage installs as a GitHub check. When you open a pull request that changes a model, it walks the downstream graph and comments with every dbt model, dashboard, and reverse-ETL sync that depends on what you changed — so you see the blast radius before you merge, not after a report goes blank.
Warehouses including Snowflake, BigQuery, Databricks, Redshift, and Postgres; transformation via dbt; and BI tools including Looker, Tableau, and Power BI, plus ingestion tools like Fivetran. Connection is read-only and the graph builds itself from your existing query history.
Most teams connect a warehouse and have a full column-level lineage graph within an hour. Because Lineage parses your existing query history, there's nothing to instrument and no migration — the map reflects how your data already flows from the first crawl.
The graph re-parses as your warehouse changes — every new model, renamed column, and dropped table is picked up on the next crawl. You're never reading a stale map; the catalog reflects the SQL running in production today, not the day someone last documented it.
Lineage is SOC 2 Type II certified, connects with read-only, least-privilege credentials, and can run single-tenant inside your own VPC on the Enterprise plan. Because it only ever touches metadata, the sensitive contents of your warehouse never enter the platform.
Connect one warehouse, watch the lineage graph draw itself, and trace your first column to its source today. Read-only, metadata-only, and no sales call required to start.