---
title: "Prefect | Skills We Assign For | Azendo"
description: "Prefect for Python workflow orchestration — dynamic flows, retries, and the Azendo roles assigned for it."
url: "https://azendo.co/skills/prefect/"
---

[Skills](https://azendo.co/skills/) Data pipelines 

# Prefect.

Prefect is a Python-native workflow orchestrator. Flows and tasks are ordinary decorated functions with dynamic structure determined at runtime, with retries, caching and observability provided by the framework.

## Where Prefect fits on a long engagement.

Dynamic workflow structure is Prefect's practical advantage. A flow whose shape depends on runtime data — processing however many files arrived, fanning out across whatever partitions exist — is ordinary code here, where statically defined DAGs require workarounds.

Because flows are plain Python, adoption cost is low for a team that already writes Python, and testing is ordinary function testing. The trade-off is fewer opinions: Prefect will not stop a team building an unmaintainable workflow, where a more prescriptive orchestrator would.

## What an assigned team does with Prefect.

Retry configuration is where reliability is actually won. A transient API failure should retry with backoff; a malformed input should not retry at all. Treating every failure the same either masks real problems or gives up on recoverable ones.

Getting that distinction right depends on knowing the specific sources and their behaviour, which is product knowledge that accumulates in a standing assignment and is set out in [how the monthly fee is built](https://azendo.co/pricing/).

## What we use Prefect for.

* Workflows whose shape depends on the data Fan-out across whatever arrived, without contorting a static DAG definition.
* Retries that distinguish failure types Backoff for transient errors, immediate failure for bad input.
* Flows tested as ordinary functions Logic verified without standing up a scheduler.

## How Prefect capacity is assigned.

Prefect work is assigned under [data engineering outsourcing](https://azendo.co/services/data-engineering/), with failure handling designed per source rather than applied uniformly.

## Roles we assign Prefect for

* [Data Engineer Data engineering](https://azendo.co/services/data-engineering/data-engineer/)

## Service lines it sits in

* [Data engineering](https://azendo.co/services/data-engineering/)

Capacity is agreed as a committed monthly capacity across a discipline, not per skill.

## Related in data pipelines

* [Spark — skill we assign for](https://azendo.co/skills/spark/)
* [Flink — skill we assign for](https://azendo.co/skills/flink/)
* [Dagster — skill we assign for](https://azendo.co/skills/dagster/)
* [Airbyte — skill we assign for](https://azendo.co/skills/airbyte/)
* [Informatica — skill we assign for](https://azendo.co/skills/informatica/)
* [Talend — skill we assign for](https://azendo.co/skills/talend/)
* [SSIS — skill we assign for](https://azendo.co/skills/ssis/)
* [Change data capture — skill we assign for](https://azendo.co/skills/change-data-capture/)

## Tell us what your roadmap needs Prefect for.

A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.

[All skills we assign for](https://azendo.co/skills/)