Querying
Query workflows retrieve live and historical device data for applications, operations, customer views, and Analytics insight streams.
Infuse DB is designed around IoT time-series access: teams need to filter timestamped records by device and fleet context, then read the raw or computed streams that answer operational questions.
Common Query Patterns
| Query pattern | Example question |
|---|---|
| Device history | What did this device report over the last 24 hours? |
| Customer fleet | Which devices for this customer are active or reporting faults? |
| Environment | How does production telemetry compare with test or field-trial data? |
| Location | Which sites or regions show changing conditions? |
| Asset group | How is this group of devices or assets performing? |
| Computed signal | What higher-value operational signal should an application or Analytics consume? |
Live And Historical Access
Live access helps applications and operators understand current fleet state. Historical access supports troubleshooting, comparison, trend analysis, and downstream Analytics use cases.
Design datasets so both access patterns remain straightforward:
- keep event timestamps consistent,
- preserve device and organisation identifiers,
- include dimensions that match customer and operational boundaries,
- store computed streams when downstream systems should not recalculate the same signal repeatedly.
Query Dimensions
Device data is most useful when it can be filtered by the way the fleet is managed. Common dimensions include customer, environment, location, asset group, board, hardware profile, and stream type.