Data modeling for Snowflake should not require a
multi-thousand-dollar enterprise license. I wanted a practical tool
that could reverse engineer a real database, give
me an editable logical and physical model, and
generate Snowflake-ready DDL. So I assigned the mission
to an autonomous AI employee and let it
build on proven open-source foundations.
This is the packaged Apple Silicon application working
with a live Chinook schema in Snowflake. The
app discovered eleven tables, sixty-four columns, primary keys,
and eleven foreign-key relationships. It converted that metadata
into an editable diagram and laid the tables
out automatically. This is not a mock-up; it
is the application running on the Mac build.
The experience starts as a desktop data-modeling tool,
not a browser tab that must stay connected
to a development server. You can begin with
a blank Snowflake model, create and edit tables
and relationships, arrange the canvas, and work with
the familiar drawDB interface inside a native Electron
shell.
Reverse Engineer Snowflake is a first-class desktop action.
The application can use an existing Snowflake CLI
profile or a deliberately bounded manual connection. Credentials
and sessions stay in Electron's main process and
are never written into the ERD project file.
Select a database, schema, and tables, then import
the result as a model you can change.
The same model generates deterministic Snowflake DDL. Database
and schema creation, table definitions, data types, comments,
primary keys, and foreign keys come from the
canonical model rather than from a one-off text
prompt. Snowflake constraints are labeled informational and not
enforced, matching the platform's behavior instead of pretending
they operate like transactional database constraints.
Projects save as native, credential-free ERD files and
reopen in a fresh desktop process. The model,
diagram layout, comments, constraints, and relationships survive the
round trip. That gives the tool a real
document workflow: reverse engineer, refine the design, save
it, reopen it later, and regenerate the DDL
without reconnecting to Snowflake.
The product story matters as much as the
feature list. The AI employee first proved the
pipeline by reverse engineering SQLite and forward engineering
the structures into Snowflake. I then assigned the
next mission: turn the open-source web application into
an installable Snowflake modeling app. It worked through
the backlog, fixed orchestration and product failures at
their source, and delivered this Mac build.
The Apple Silicon package has passed one hundred
fifteen automated checks plus a live acceptance run:
connect, discover, reverse engineer, save, reopen, and regenerate
forward DDL. The current build is unsigned and
intended for private preview. Linux, Windows, signing, and
notarization remain future release work. To follow the
project or discuss a Snowflake modeling use case,
visit Lee Harrington's project hub.