Pedal Works — Bicycle Shop Chain
Built from a bundled worked brief, so it needs no model at all. 8 tables, camelCase, MariaDB. This is the shape the ITM 209 exams used.
Pipeline Brief → schema → seeded rows
A business context in a sentence or two becomes a working relational dataset: schema, realistic skewed data, a runnable SQL script, zipped CSVs and a narrative that matches the data. The model designs the schema; a seeded generator writes the rows, so the same brief and seed reproduce the same dataset byte for byte.
Built from a bundled worked brief, so it needs no model at all. 8 tables, camelCase, MariaDB. This is the shape the ITM 209 exams used.
Pipeline Brief → schema → seeded rows
From a single sentence: 9 tables, 5,852 rows, farm-to-cup lineage with batch and order line junctions. camelCase, MariaDB.
Pipeline Rich field complexity, 7–10 tables
Brewing, wholesale distribution and taproom sales in one schema. Backs the second SQL assignment, whose reference answers were executed against exactly these rows.
Pipeline Backs an assignment with verified expected_rows
The same pipeline in a different house style and dialect — 8 tables, 4,404 rows, snake_case identifiers, PostgreSQL DDL.
Pipeline Naming style and dialect are inputs, not rewrites
Complete instructor packages: the assignment students receive, the rubric that scores it, the full answer key, and a private TA guide — all from one specification.
Filtering, joins and aggregation across a bicycle retail and repair chain. Every reference solution was executed against the generated data, so the rubric records the exact row count each correct answer returns.
Generated by SQL Assignment Generator · Pedal Works dataset
Subqueries, CASE expressions and multi-table analysis across brewing, distribution and taproom operations, weighted toward the harder end.
Generated by SQL Assignment Generator · Cross-Grain dataset
The same package for spreadsheets, plus the workbooks themselves — a starter with the answer cells highlighted, and a solution with every formula, named range and chart already in place.
Lookup, conditional logic, summarisation and charting over a season of commercial landscaping jobs.
Generated by Excel Assignment Generator
Native formulas and Python in Excel side by side. The =PY() tasks read the grid with xl(), aggregate with pandas, and return a matplotlib figure into the cell.
Generated by Excel Assignment Generator
Worksheet-by-worksheet specifications with required shelves, filters and caption minimums, plus the extract and a typed .tds datasource.
Five worksheets and a dashboard over the roastery's 1,200 roast batches. The .twbx starter carries the data inside it, with every field typed, roles assigned, and the required worksheets pre-named and pre-marked.
Generated by Tableau Assignment Generator · Bean & Bloom dataset
Report pages and DAX measures, plus a .pbip starter project carrying the semantic model and relationships.
A three-page report with DAX measures and drill-through. The starter is a .pbip project: the 8-table model, its relationships and the named report pages are already built, so students build visuals rather than plumbing.
Generated by Power BI Assignment Generator · Northlake dataset
Each assignment package is generated together from a single specification, so the rubric, the answer key and the TA guide cannot drift from the assignment. Rubrics are validated against their own reference solutions before release — a requirement the reference answer would fail is a defective requirement, not a student error.