What is STACKIT Workflows?
STACKIT Workflows is a fully managed workflow orchestration service built on Apache Airflow. Users author, schedule, and monitor complex data workflows as Directed Acyclic Graphs (DAGs) in Python, without needing to provision or maintain their own Airflow infrastructure. Operations run in German data centers.
Core Features
- Fully managed Apache Airflow infrastructure with a documented upgrade path to Airflow 3
- DAG authoring in Python; a web-based development environment has been announced
- Dynamic resource scaling based on workload
- KubernetesPodOperator for isolated task execution with custom Docker images
- Operators and decorators for Spark jobs and custom Python code
- Integration with STACKIT Spark and predefined STACKIT Observability dashboards
Typical Use Cases
Data Pipeline Orchestration: Teams coordinate multi-stage ETL processes between databases, data lakes, and analytics platforms, including error handling and data quality checks.
ML Automation: ML pipelines are defined as DAGs: data preparation, feature engineering, model training, validation, and deployment can be orchestrated as a connected workflow.
Spark-based Processing: Spark jobs are controlled directly from Airflow DAGs via specialized operators.
Benefits
- No need to operate your own Airflow infrastructure
- No lock-in, since Apache Airflow is an established industry standard
- Isolated, reproducible task execution via Kubernetes
- Operated in German data centers
Integration with innFactory
As an official STACKIT partner, innFactory supports you in designing and implementing data pipelines: from DAG architecture and error handling to integration with the STACKIT data ecosystem.
Typical Use Cases
Frequently Asked Questions
What is STACKIT Workflows?
STACKIT Workflows is a fully managed service built on Apache Airflow that lets you author, schedule, and monitor complex data workflows as Python DAGs. STACKIT operates the Airflow infrastructure, so you don't need to run your own Airflow deployment.
Do I have to define workflows in Python code, or is there a visual editor?
Currently, DAGs are authored as Python code. A web-based development environment has been announced according to STACKIT documentation but is not yet available.
Which Airflow version is supported?
STACKIT Workflows is built on Apache Airflow and offers a documented upgrade path to Airflow 3. Refer to the official documentation for the current version status.
How can Workflows be combined with other STACKIT services?
Workflows integrates with STACKIT Spark for data extraction and processing, as well as with STACKIT Observability via predefined dashboards. Task execution can run through the KubernetesPodOperator in isolated Kubernetes pods with custom Docker images.
What does STACKIT Workflows cost?
The service is billed on a usage basis (pay-per-use). The official STACKIT pricing page is authoritative for current prices.
Note: All product information on this page has been compiled with care, but is provided without guarantee and may be outdated or incomplete. Cloud services evolve rapidly — features, pricing, SLAs, and availability change frequently. Authoritative and up-to-date information can only be found on the official product page of STACKIT (official documentation). This page does not represent an offer by STACKIT.
