prefect-workflows

Build a retryable ETL workflow with Prefect 3 by using @flow and @task decorators to structure ingestion and transformation logic, then configure retries, timeouts, and caching policies to ensure resilient pipeline execution.

Deploy Prefect flows to Kubernetes or ECS work pools by defining deployment configuration in prefect.yaml or using serve(), which enables workers to poll and execute scheduled runs across different infrastructure backends.

Caching in Prefect avoids expensive data reads by storing completed task results. You configure caching policies on individual tasks using the @task decorator to reuse previous outputs when inputs remain unchanged.

Yes, you can trigger Prefect workflows automatically using event-driven triggers. Prefect 3 supports configuring deployments to listen for external events, enabling data pipelines to react dynamically to upstream system changes.

Run parallel data transformations in a Prefect flow by using submit() and map() methods on tasks. These methods enable fan-out workloads, allowing concurrent execution of multiple task instances across available workers.

Monitor pipeline failures and completion states in Prefect by configuring state hooks. Attach custom failure and completion hooks to flows and tasks to observe execution status and trigger automated operational reactions to success or failure.