Privacy-first ETL for spreadsheet-heavy teams

Build ETL pipelines locally, keep your data private, and export SQL when needed.

RustyWrench helps operations, finance and data-adjacent teams turn spreadsheet logic into visual ETL workflows that run entirely on their own machine. No forced cloud transfer, no immediate platform migration, and a much easier path to cloud pipelines later because the flow can be exported to SQL.

Privacy

ETL runs locally, so your raw operational data stays under your control.

SQL export

Export the full pipeline to SQL when it is time to productionize the workflow.

Platform Migration

Move later to warehouses or cloud providers without starting from zero.

Privacy-first ETL
See the workflow, not just the promise
The visual pipeline can start with local files, stay on your own machine, and later the transformation logic can be exported to SQL for cloud execution.
Workflow snapshot

This is where your pipelines will be created.

Local execution
Video preview
Why teams start here
A privacy-first entry point for teams that still live in spreadsheets.
Many ETL projects start with operational people who understand the business process but do not want to jump straight into a cloud-only stack. RustyWrench gives those teams a clearer first step: keep data local, model the logic visually and graduate to SQL when the process is stable.
Keep sensitive data on your own machine

RustyWrench runs your ETL locally, so spreadsheets, CSVs and operational files do not need to leave your environment just to become a repeatable pipeline.

Export the full pipeline to SQL

Map transformations visually, validate the logic, then export the resulting pipeline to SQL when you are ready to move into a warehouse or cloud workflow.

Start with non-technical users, migrate later

It is a practical entry point for teams that still think in spreadsheets today and want a cleaner path into cloud pipelines tomorrow, without locking into one provider.

How it works
Start visually, then move with confidence.
The goal is not to trap you in a desktop tool. It is to make pipeline design easier for real teams, then turn that work into a cleaner path toward SQL and cloud execution.
1
Bring local files into one clear flow

Start from spreadsheets, CSVs, parquets and JSON data without provisioning cloud infrastructure first.

2
Shape the logic visually

Turn business logic into steps that analysts, operators and data-adjacent teams can follow without reading code all day.

3
Validate the output locally

Run the ETL on your own machine with explicit execution feedback before handing the process to a larger stack is required.

4
Export to SQL when the pipeline matures

Once scalability is needed, move it into the cloud or another provider with much less rework because the flow already exists in SQL form.

Platform migration path
From messy spreadsheets to cloud-ready pipelines.
1
Start where the work already happens

Use RustyWrench to organize spreadsheet-heavy ETL before your team is ready for a heavier platform decision.

2
Standardize the transformation logic

Replace ad hoc spreadsheet routines with a repeatable visual pipeline that everyone can inspect.

3
Export to SQL and keep your options open

Use SQL as the bridge from local workflows into warehouses, schedulers and cloud-native data stacks.

4
Move to the provider that fits later

Adopt cloud execution when you need it, not before, and without rebuilding the whole pipeline from scratch.

Waitlist open
Join the waitlist for privacy-first ETL.
Join early if your team works with sensitive operational data, lives in spreadsheets today, or wants a simpler bridge from local workflow design into SQL-based cloud pipelines.
Built for real business users

RustyWrench is especially useful for teams that understand the process deeply but do not want the first step to be hand-written cloud infrastructure.

Move on your own terms

Use the desktop app as a practical entry point, then migrate to the cloud or another provider later with far less friction because the pipeline can be exported to SQL.