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Temporal
Durable workflow execution — workflows survive crashes and resume exactly where they left off.
VS
At a Glance
| Attribute | Temporal | Apache Airflow |
|---|---|---|
| License / Pricing | Open Source | Open Source |
| Type | plateng | data |
| GitHub Stars | — | — |
| Rating | 4.7/5 | 4.5/5 |
| Key Features | 6 listed | 6 listed |
| Integrations | 4 listed | 5 listed |
| Categories | Workflow Engines | Workflow Orchestration |
Key Features
Temporal
- Durable execution — workflow state persisted automatically, resumes after any failure
- Language-native SDKs for Go, Python, Java, TypeScript, and .NET
- Activities for encapsulating unreliable external calls with configurable retries
- Signals and queries for real-time interaction with running workflows
- Timers that work across process restarts — schedule events days or weeks into the future
- Temporal Cloud fully managed service with 99.99% SLA
Apache Airflow
- DAGs defined as Python code with rich scheduling expressions (cron, timetables)
- Rich UI for monitoring task execution, logs, and Gantt charts
- 250+ provider packages for AWS, GCP, Azure, Spark, dbt, and more
- Dynamic task mapping for parallel fan-out on variable-size inputs
- Managed Airflow via Amazon MWAA, Google Cloud Composer, and Astronomer
- XCom for passing data between tasks with flexible backend storage
Real-World Use Cases
Temporal
Long-running order fulfillment workflow
Define a workflow that: reserves inventory → charges payment → ships → sends confirmation
User onboarding sequence with wait steps
Start a workflow when a user signs up
Apache Airflow
Orchestrate a dbt + Spark ETL pipeline
Define a DAG with SparkSubmitOperator to run a transformation job
ML training pipeline orchestration
Create tasks for data ingestion, feature engineering, model training
Integrations
Temporal
kubernetesbackstagedatadogprometheus
Apache Airflow
apache-sparkdbtairbytesnowflakegreat-expectations