Cross-Platform Transformation Engine
Describe a transformation once — "sum sales by region", "top 10 customers by revenue", "keep email and region only" — and get the equivalent in Excel, SQL, Power Query, pandas, and DAX, with the caveats that trip people up on each platform.
Code generation happens in your browser — nothing is uploaded.
Tool interface
Generated transformation
Cross-platform caveats
Introduction
Analysts constantly re-express the same logic in different tools: a SQL query becomes an Excel formula, then a Power Query step, then pandas, then a DAX measure. Each translation is a chance for subtle bugs. This engine generates all versions from one description and highlights the caveats that differ between platforms.
How it works
- Pick a transformation type and fill the visible fields.
- The engine builds the equivalent expression for each supported target.
- Targets where a transformation has no natural form (for example, sorting in DAX) are honestly marked "not applicable".
- Caveats list the behavioral differences you must know before trusting the output.
How to use
- Choose the transformation (aggregate, filter, sort, keep columns, rename, dedupe, computed column).
- Fill in the fields — defaults show a working example.
- Click Generate for all platforms.
- Copy the version you need, and read the caveats first.
Practical example
"Sum sales by region" becomes SUMIF in Excel, GROUP BY in SQL, Table.Group in Power Query, groupby().agg() in pandas, and a SUMX measure in DAX.
Use cases
- Migrating spreadsheet logic into SQL or a BI model.
- Reproducing a SQL report in pandas for a notebook.
- Documenting what a transformation means across the team's tools.
- Teaching the relationships between platforms.
Best practices
- Always read the caveats — NULL handling and tie behavior differ between platforms.
- Keep column names consistent across tools so generated code pastes cleanly.
- Verify generated code against a small sample before applying it to full data.
Limitations
- Covers the seven most common transformation patterns, not arbitrary expressions.
- SQL output targets standard syntax; vendor dialects may need small tweaks.
- DAX supports aggregate-style patterns only (row-level operations don't map).
FAQ
Is my data uploaded anywhere?
No. Generation is pure local string building — no data is involved at all.
Why is some target marked "not applicable"?
Some platforms genuinely cannot express certain operations (for example, DAX has no row-ordering concept). The tool tells you instead of emitting misleading code.