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| Question | Outcome |
|---|---|
| See requirement #1 |
2. Does Distill require a specific backend (Elastic) or can it go to Solr/Lucene | Underlying data store needs to support key value pairs |
| 3. How do we support Windows Users? | Integrate testing across platforms See requirement #9 |
| 4. How do we provide the "average" data scientist enough out of the box packages, modules to be minimally viable out of the box? | |
| 5. Roadmap for supporting packages and Anaconda distribution | |
| 6. Migrate to Django from Flask? | |
| 7. Is Distill simple python, or does it run as a service (or on a webservice) by design? | |
| 8. Does Distill manage scale in its connections to other datastores, or does it rely soley on Lucene based services (Elastic)? | Distill's querying is dependent on how well Elasticsearch scales on query. |
| 9. Does Distill remain tethered outright to Elastic? | See requirement #2 |
| 10. TLS or SSL: Modern vs. Legacy network support. |
Analytics & Processing Examples
These examples are here for drawing out higher-level goals for Distill's functionality. This section can be removed once the goals have been solidified.
- Build intervals from matching sequences of raw events
- Filter out unwanted events
- Noisy/irrelevant events
- May be conditional on neighboring events
- "dangling" events (e.g. a stop event with no corresponding start)
- Noisy/irrelevant events
- Collapse duplicate events into a single event (when is this preferable to creating an interval?)
- Create "sandwiches" (a set of events bookended by, e.g., a related start and stop event)
- Replace some logs/data with other logs/data
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