Engineers need custom stateful stream logic in Python. What fits?
Why D is the answer
Spark structured streaming
Structured streaming gives code-level control. Eventstreams suit low-code routing.
In detail
- Spark structured streaming supports custom stateful logic in Python.
- Choose eventstreams for simple routing.
Use case
An illustrative scenario, not a real incident.
Fraud logic must remember each card's recent activity, which needs state. Structured streaming in Python gives that control.
See it in different ways
flowchart LR A["Event source"] --> B["Eventstream"] B --> C["Eventhouse"] B --> D["Lakehouse"] C --> E["KQL queries and alerts"]
flowchart LR A["Events"] --> B["Eventstream"] B --> C["Eventhouse"] B --> D["Activator alert"] A --> E["Spark structured streaming"]
flowchart TD
Q{"What is needed?"}
Q -->|"Low-code routing"| A0["Eventstream"]
Q -->|"Custom stateful logic"| A1["Spark structured streaming"]Key point: Structured streaming gives code-level control.
Why the other options are wrong
- A. A daily dataflow refresh
- A daily refresh is not streaming.
- B. A warehouse stored procedure on a schedule
- A scheduled procedure is not streaming.
- C. A deployment rule
- A rule changes deployment settings.