Synthetic Data for Streaming and Media
Streaming data is a catalog plus an event firehose, with popularity that is anything but uniform. Misata generates a heavy-tailed view distribution so a few titles dominate, bounds watch time by each title's runtime, and ties every watch event to a real user and title.
The tables Misata generates
usersSubscriptions, plans, watch-count roll-upstitlesGenres from real vocabulary, runtime, heavy-tailed popularitywatch_eventsTimestamps, watch time bounded by title runtimesubscriptionsPlan-priced, status lifecycleWhat holds true, every time
- Title popularity follows a heavy tail, like real catalogs
- Watch time never exceeds a title's runtime
- Every watch event references a real user and title
- Genres use real vocabulary, not filler labels
Frequently asked
Do I need real streaming data to generate this?
No. Misata builds the dataset from a specification, not a sample. There is no real streaming data to source, anonymize, or leak. You describe the tables you need and the engine constructs them with referential integrity and realistic distributions.
Is the generated streaming data privacy safe?
Yes, by construction. Nothing is learned from real records, so there is no membership to infer and nothing to leak. It runs entirely on your machine with no API key for the core engine.
Can I control the outcomes, like rates and totals?
Yes. Declare a target such as a monthly volume curve or an event rate and Misata produces rows that hit it exactly, while foreign keys stay intact and roll-up columns reconcile after a JOIN.
Choosing a tool? How Misata compares