Know what breaks
before your data model changes.
Sat-Impactcheck tests every satellite record you have already received against your current and your new data model. It shows what breaks, why, and what can be converted automatically, before you migrate.
The first object in the satellite catalogue with a six-digit number. The TLE format, standard since the 1960s, only has five positions for it.
How it works
Pick your current model and the new one. Every received record is tested against both, and gets one of four outcomes, with an example and a fix for every field that changes.
Valid under the new model, nothing to do.
Converted automatically: text to number, TLE epoch to date, Alpha-5, units.
Convertible, but fields disappear. Archive them first.
Cannot be fixed automatically: the model or the source has to change.
Your own data, not a sample
Live orbital data from CelesTrak and SatNOGS, deep-space positions from NASA JPL Horizons, stored in your own PostgreSQL or any SQL database.
Every model version kept
Data and model versions are stored insert-only in a Data Vault (hubs, links, satellites), so every check is traceable and repeatable.
See it, not just read it
A 3D globe with every tracked satellite and piece of debris, the solar system with probes and rovers, and the impact check side by side.
46 documented cases. An honest count.
From Mariner 1 to catalogue number 100000: data and model failures in spaceflight, each with sources. We sorted them by what a check before the change could have done.
A model, unit or parameter set changed and could have been tested in advance.
A rule would have caught it, but it failed live on board.
Hardware, assembly or live commands: no data tool fixes that.
Not only for space
The same check works for any data warehouse: finance, HR, healthcare. bi-Wooning has built data warehouses from source to dashboard for years; Sat-Impactcheck is that craft, applied where a model change costs the most.
Planning a migration to OMM?
Let us run the check on your own data first.