One of the highest-leverage demo assets we're missing: a ready-to-open .pbix file that loads a Spindle-generated retail dataset.
What to do
Run:
pip install sqllocks-spindle pyarrow
spindle generate retail --scale large --seed 42 --output ./demo-data/ --format parquet
(Note: pyarrow is required for --format parquet — see Issue Parquet output requires pip install pyarrow (not auto-installed) #6 .)
Open Power BI Desktop, Get Data → Folder → ./demo-data/ , load all tables
Build a small star-schema model (Customer + Product + Date dims, Order facts)
Add 3–4 visuals that show off realistic patterns:
Seasonal sales line chart (Spindle generates real seasonality)
Geographic map (Spindle ships real US lat/lng addresses)
Pareto / top-customers chart
Customer LTV histogram
Save the .pbix and add it to examples/powerbi/spindle-retail-demo.pbix
Add a short examples/powerbi/README.md with screenshots
Why this is a good first issue
No code required — just Power BI Desktop
Showcases the "data looks like production data" claim from the README
Reusable in launch posts and tutorials
If you don't have Power BI Desktop, a .pbit template (without data) is equally welcome.
One of the highest-leverage demo assets we're missing: a ready-to-open
.pbixfile that loads a Spindle-generated retail dataset.What to do
Run:
(Note:
pyarrowis required for--format parquet— see Issue Parquet output requirespip install pyarrow(not auto-installed) #6.)Open Power BI Desktop, Get Data → Folder → ./demo-data/, load all tables
Build a small star-schema model (Customer + Product + Date dims, Order facts)
Add 3–4 visuals that show off realistic patterns:
Save the
.pbixand add it toexamples/powerbi/spindle-retail-demo.pbixAdd a short
examples/powerbi/README.mdwith screenshotsWhy this is a good first issue
If you don't have Power BI Desktop, a
.pbittemplate (without data) is equally welcome.