You need fast queries over streaming telemetry using KQL. Which store fits?
Why A is the answer
An eventhouse
Eventhouses store and query streaming and time-series data with KQL.
In detail
- An eventhouse gives fast KQL queries over streaming data.
- KQL is built for time-based analysis.
Use case
An illustrative scenario, not a real incident.
A factory streams machine readings every second. An eventhouse stores them, and KQL queries answer in moments.
See it in different ways
flowchart TD
A{"Main workload"} -->|"Spark and files"| B["Lakehouse"]
A -->|"T-SQL and transactions on tables"| C["Warehouse"]
A -->|"Streaming and time-series with KQL"| D["Eventhouse"]mindmap
root((Data stores))
Lakehouse
Spark
Files and tables
Warehouse
T-SQL
Transactions
Eventhouse
KQL
Streaming telemetryflowchart LR S0["Machine readings"] --> S1["Eventstream"] --> S2["Eventhouse"] --> S3["KQL query"]
Key point: Eventhouses serve streaming and time-series data.
Why the other options are wrong
- B. A deployment pipeline
- That is for promotion.
- C. A warehouse with nightly loads
- Nightly loads are not streaming.
- D. A dataflow with a daily refresh
- A daily refresh is not streaming.