Reference Entry
Slicing and Dicing Open Data with Querylight TS
Guides · querying · order 33
Use small open-data snapshots to prototype interactive analytics in the browser with Querylight TS.
Relevant APIs
Slicing and Dicing Open Data with Querylight TS
Open data is useful for demos because it behaves like real product data in one important way: it usually arrives as raw records.
That makes it a good fit for Querylight TS.
The dashboard demo uses three toy snapshots:
- World Bank indicators
- USGS earthquakes
- Open-Meteo weather history
These are not meant to support serious analysis. They are there to show how to go from raw records to interactive exploration.
Why open data works well for this pattern
- the payloads are public
- the data shapes are realistic
- the rows have enough structure for filtering
- you can tell a coherent story with small samples
A useful workflow
- Choose one or two representative endpoints.
- Download a small snapshot at build time.
- Normalize the fields you care about.
- Keep the raw record shape recognizable.
- Index exact-match, numeric, date, and geo fields as needed.
- Drive charts from subset aggregations.
That gives you a realistic prototype without a large backend.
Good questions to ask while exploring
- which dimensions make sense as filters?
- which metrics deserve summaries?
- which distributions are worth charting?
- what vocabulary becomes significant in a slice?
- what is the minimum local document shape that still feels expressive?
Those are product questions as much as they are technical ones.
Keep the disclaimers honest
If you use open data in a demo:
- state clearly that the datasets are toy snapshots
- attribute the source visibly
- avoid claiming analytical correctness you did not validate
- treat the visualizations as examples of capability, not conclusions
That is the stance the dashboard demo now takes.