Directed Acyclic Graph (DAG)
The short answer · 19 words
In short
A DAG maps directional dependencies without a closed circle – an important basis for plannable data and automation processes.
Flow-alerta.cab (2026): Directed Acyclic Graph (DAG). https://flow-alerta.cab/en/glossar/directed-acyclic-graph-dag. Editorially reviewed on: 10 October 2026.
How much detail would you like?
Practical
DAGs make dependencies visible, allow parallel steps and prevent logically impossible cycles. This helps with data pipelines, ETL processes and repeatable automations.
The “Import bills” task must run before “check amounts”; “check amounts” and “archive receipts” can then be modeled dependently or in parallel.
Companies model recurring data imports, reports or approvals as DAGs. In SMEs, a manageable graph with clear responsibilities, repetition rules and monitoring is worthwhile.
At a glance
- Core
- A directed acyclic graph consists of nodes and directed edges. Acyclic means: If you follow the arrows, you never get back to the starting node via a closed path. Workflow systems use DAGs to derive permitted execution orders from dependencies.
- Use
- DAGs make dependencies visible, allow parallel steps and prevent logically impossible cycles. This helps with data pipelines, ETL processes and repeatable automations.
- Example
- The “Import bills” task must run before “check amounts”; “check amounts” and “archive receipts” can then be modeled dependently or in parallel.
- Limits
- Do not equate DAG with the entire workflow system. Hidden cycles, missing dependencies, non-idempotent tasks and unclear error handling lead to unstable processes despite correct graphing.
In context
Questions about this term
What does acyclical mean?
There is no directional circular path.
Is every workflow a DAG?
No; a DAG is a structure that is particularly suitable for finite dependency sequences.
Can a DAG Parallel Steps?
Yes, if no dependency blocks them.
All details and background
A directed acyclic graph consists of nodes and directed edges. Acyclic means: If you follow the arrows, you never get back to the starting node via a closed path. Workflow systems use DAGs to derive permitted execution orders from dependencies.
ELI5 – Explained simply
Think of tasks as boxes with arrows. An arrow says what needs to be done first. No arrowway can run in circles.
Why it matters in practice
DAGs make dependencies visible, allow parallel steps and prevent logically impossible cycles. This helps with data pipelines, ETL processes and repeatable automations.
Example
The “Import bills” task must run before “check amounts”; “check amounts” and “archive receipts” can then be modeled dependently or in parallel.
Pitfalls and limits
- Do not equate DAG with the entire workflow system. Hidden cycles, missing dependencies, non-idempotent tasks and unclear error handling lead to unstable processes despite correct graphing.
Origin & history
Apache Airflow uses Dag to model tasks, dependencies, and execution rules. The documentation indicates that this workflow concept goes beyond the purely mathematical data structure.
Use in mid-sized companies
- Companies model recurring data imports, reports or approvals as DAGs. In SMEs, a manageable graph with clear responsibilities, repetition rules and monitoring is worthwhile.
Related terms
- Workflow orchestration
- Data pipeline
- Topological sorting
- ETL
- Idempotency
Matching Flow-alerta service
AI assistants & automationProcess analysis and automation: record dependencies, design robust workflow models and define monitoring and error paths.Matching tool / use case
View use casesApache Airflow for time- or event-driven data pipelines; use case: daily data import with testing, transformation and notification.Sources
- airflow.apache.orgApache Airflow, Dags
Quellenprüfung: 10.10.2026. Definition und dokumentierte Einordnung abgeglichen; Beispiele beschreiben mögliche Anwendungen.
Editorially reviewed on: