Apache Airflow vs Azure Data Factory
Compare Apache Airflow and Azure Data Factory for workflow authoring, dependencies, hybrid execution, retries, historical reruns and operating responsibility.
Which pipeline approach fits?
Use Airflow when Python-defined workflows and control of the orchestration environment fit the team. Use Azure Data Factory when managed pipeline activities, linked services and its integration runtimes fit the source-to-destination design. Both can coordinate work performed elsewhere. Neither an automatic retry nor a successful workflow status proves that rerunning a task leaves correct, duplicate-free output.
Seven differences that change the shortlist
| Buying question | Apache Airflow | Azure Data Factory |
|---|---|---|
| Authoring approach | Python-defined tasks and dependency graphs | Pipelines with activities, parameters and linked services |
| Operating model | Self-managed orchestrator or separate managed Airflow provider | Managed Azure service with selected integration runtimes |
| Execution boundary | Workers execute tasks or submit work to other systems | Activities move data or run the selected transformation/compute |
| Hybrid reach | Design worker or task access to internal systems | Design the integration runtime and linked-service access |
| Recovery | Configure retries, historical runs and task behaviour | Configure activity retries, pipeline parameters and write behaviour |
| Cost scope | Control plane, workers, logs, hosting and external jobs | Orchestration, runtimes, movement, transformations and external jobs |
| Related service | Managed Airflow products have separate commercial terms | Data Factory in Fabric has a separate product and pricing scope |
Four worked buying briefs
These are hypothetical scenarios with explicit assumptions. They do not establish equal capabilities, measured usability or a negotiated price.
| Requirement | Proposed exercise | Accept when |
|---|---|---|
| Dependency correctness | Check the expected business date before downstream work | The right data-ready condition is enforced |
| Rerun safety | Reprocess one approved test date | The destination contains the intended result once |
| External jobs | Trace a submitted job through completion or cancellation | No unowned processing remains |
| Recovery ownership | Review control-plane and runtime recovery | The operator can identify affected pipeline work |
Daily dependencies
Can the pipeline wait for the right upstream result?
Apache Airflow: Define the task dependencies and the required data-ready condition.
Azure Data Factory: Define activity dependencies and the required input-ready condition.
A predecessor finishing successfully may not prove that the correct business date is complete.
Historical rerun
Can the owner safely process a missed date?
Apache Airflow: Parameterise the date and review the relevant run and task behaviour.
Azure Data Factory: Parameterise the pipeline and review the destination write strategy.
Use an approved historical test partition; do not overwrite a live reporting period during evaluation.
External processing
Who owns a long-running transformation after the scheduler starts it?
Apache Airflow: Record the submitted job identity, completion check and cancellation behaviour.
Azure Data Factory: Record the external activity’s job identity, completion check and cancellation behaviour.
Confirm that an orchestration timeout does not leave untracked, chargeable work running.
Runtime outage
Can someone restore scheduling and explain the affected data?
Apache Airflow: Review metadata, configuration, workers and any managed-provider responsibilities.
Azure Data Factory: Review service dependencies and any customer-operated integration runtime.
Define recovery ownership and reconcile missed or repeated work; this is a proposed exercise, not a measured recovery result.
Schedule the right work, not just the next task
A workflow describes more than a sequence of green boxes. It needs a business date, data-ready condition, credentials and a destination write contract. Define those values before deciding whether Python authoring or managed pipeline activities are the better fit.
Airflow can coordinate jobs in other systems, and Data Factory can invoke external compute. Keep those jobs visible in the operating design. Identify how their status is tracked and who can cancel or repair them if orchestration fails halfway through.
Design reruns before the first incident
A retry repeats an action; it does not make the action safe. A task that appends rows or calls an external service may create duplicates when restarted. Choose the write, deduplication or checkpoint strategy and reconcile a controlled rerun against the expected output.
For Airflow, record the control-plane and worker operating arrangement, including any managed provider. For Data Factory, record the chosen integration runtime and related services, and distinguish the Azure service from Fabric. Include both orchestration and execution costs so the estimate follows the work all the way to its destination.
Questions before you choose
Does Airflow process all pipeline data inside the scheduler?
No. Tasks run on execution infrastructure or submit work to other systems. Do not treat the scheduler as the data-processing engine.
Does an automatic retry prevent duplicate output?
No. The task and destination need an appropriate write and recovery design.
Is self-managed Airflow free to operate?
The upstream software is free, but infrastructure, maintenance, execution resources and external services still cost money.
Your practical evaluation pack
Download the pipeline checklist (CSV)
Four proposed checks with blank evidence and owner columns. Record the actual deployment, package, data volume and recovery requirements in your evaluation.
Turn the comparison into your own evaluation
Use the same criteria and priorities for both products, keep unknowns unassessed and record the plan and evidence behind your ratings.
Build a Apache Airflow vs Azure Data Factory scorecard →Keep the assumptions with the figures
Use the date, billing cycle, units and limits with each example. Check current account terms before purchasing. Unverified prices and model costs stay unpriced.
Download the buying examples as JSON ↓Share the comparison
Use these examples when discussing the shortlist with your team.