100% browser-only · nothing leaves your tab
SQL Lineage Visualization Tool
A free, online SQL viewer that turns your SQL files into interactive SQL lineage — see how tables, views, CTEs, and columns depend on each other as an auto-laid-out DAG. No sign-up, no data upload, nothing leaves your browser.
Paste your SQL to see its lineage
Drop in a query (or use the toolbar to add files or the sample). The dependency graph builds the moment you submit.
1 · Parse
A lightweight in-browser extractor pulls table definitions, CTEs, and FROM/JOIN references from your SQL.
2 · Resolve
References are matched across every uploaded file. Unresolved upstream sources render as dashed boundary nodes.
3 · Render
React Flow with Dagre auto-layout draws the dependency graph left-to-right. Pan, zoom, and expand columns.
A free SQL lineage tool that runs entirely in your browser
Understanding SQL lineage — how data flows
from source tables through transformations into your final outputs — is one of the
hardest parts of working with a modern data stack. When a dashboard number looks
wrong or a pipeline breaks, you need to trace dependencies fast. This
SQL lineage visualization tool does exactly
that: drop in your .sql
files and instantly get an interactive diagram of every table, view, and CTE and the
relationships between them. It works as a general-purpose
SQL viewer too — a clean way to read,
explore, and make sense of unfamiliar SQL without opening a database client.
Why choose this SQL lineage tool
Most SQL lineage tools are heavy, hosted platforms that require you to connect a warehouse, create an account, and send your queries to a third-party server. That is a non-starter when your SQL contains proprietary schemas and business logic. This is a free SQL viewer and lineage tool that flips that model: all parsing and rendering happen locally in your browser tab. Nothing is uploaded, nothing is stored, and there is no sign-up. It is the fastest way to get SQL viewer online functionality with lineage on top — open the page and start visualizing in seconds.
Table and column lineage from your SQL
The lightweight extractor recognizes the SQL patterns that show up in real ETL and
analytics code: CREATE TABLE AS
SELECT, CREATE VIEW,
INSERT INTO … SELECT, common
table expressions (WITH …),
and FROM /
JOIN references. Upload
several files at once and dependencies are resolved across them, so you can map an
entire pipeline in a single view. Expand any node to inspect column-level lineage and
follow a field from its origin all the way downstream. Derived and aggregated columns
are flagged so you can see where logic is applied.
A lightweight alternative to Python SQL lineage libraries
If you have reached for a Python SQL lineage
library like sqllineage or
sqlglot to script out
dependencies, this tool is the zero-setup visual companion: no
pip install, no notebook, no
environment to manage. Paste or drop your SQL and get an instant, shareable picture of
the graph. It is dialect-agnostic and handles the common ANSI-style patterns used in
Snowflake, BigQuery, Postgres, and Spark SQL, making it a practical everyday
SQL viewer for data engineers, analytics
engineers, and anyone who needs to reason about how their SQL fits together.
Frequently asked questions
Does my SQL leave my browser?
No. Parsing and graph rendering run entirely in your browser tab via JavaScript. No file is uploaded to any server, so proprietary schemas and business logic stay private.
Is this SQL viewer free?
Yes. It is a completely free online SQL viewer and lineage tool. There is no sign-up, no paywall, and no data upload.
What SQL patterns are supported?
CREATE TABLE AS SELECT, CREATE VIEW AS SELECT, INSERT INTO … SELECT, CTEs (WITH …), FROM/JOIN references, and cross-file resolution. Derived and aggregated columns are flagged. See the roadmap for column-level lineage.
Can I upload multiple files?
Yes. Drop several .sql files and references are resolved across them. Tables referenced but never defined show up as dashed boundary nodes.
Which dialects work?
The V1 extractor is dialect-agnostic and covers the common ANSI-style ETL patterns (Snowflake, BigQuery, Postgres, Spark SQL). Dialect-specific syntax is best-effort in V1.