Apache Airflow
E457354
Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring complex workflows and data pipelines.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Apache Airflow canonical | 6 |
| Apache Airflow DAGs | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4654879 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Airflow Context triple: [TensorFlow Extended, supportsOrchestrator, Apache Airflow]
-
A.
Apache Oozie
Apache Oozie is a workflow scheduler system designed to manage and coordinate Hadoop jobs such as MapReduce, Pig, and Hive in complex data processing pipelines.
-
B.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
C.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
D.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for developing and executing batch and streaming data processing pipelines, based on Apache Beam, within the Google Cloud ecosystem.
-
E.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Apache Airflow Target entity description: Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring complex workflows and data pipelines.
-
A.
Apache Oozie
Apache Oozie is a workflow scheduler system designed to manage and coordinate Hadoop jobs such as MapReduce, Pig, and Hive in complex data processing pipelines.
-
B.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
C.
Apache Beam
Apache Beam is an open-source unified programming model for defining and executing batch and streaming data processing pipelines across multiple execution engines.
-
D.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for developing and executing batch and streaming data processing pipelines, based on Apache Beam, within the Google Cloud ecosystem.
-
E.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
- F. None of above. chosen
Statements (59)
| Predicate | Object |
|---|---|
| instanceOf |
data pipeline orchestration tool
ⓘ
open-source software ⓘ workflow orchestration platform ⓘ |
| abbreviation | Airflow ⓘ |
| category |
Apache Software Foundation project
ⓘ
data engineering tool ⓘ workflow management system ⓘ |
| developer | Apache Software Foundation ⓘ |
| feature |
CeleryExecutor
ⓘ
DAG-based workflow definition ⓘ KubernetesExecutor ⓘ LocalExecutor ⓘ REST API ⓘ SequentialExecutor ⓘ backfilling ⓘ extensible operators ⓘ hooks and sensors ⓘ metadata database ⓘ monitoring and alerting ⓘ pluggable executors ⓘ plugins system ⓘ role-based access control ⓘ scheduler ⓘ task dependencies ⓘ task retries ⓘ web-based user interface ⓘ workers ⓘ |
| integratesWith |
Amazon Web Services
ⓘ
Apache Hadoop ⓘ
linked to:
Hadoop
Apache Spark ⓘ BigQuery ⓘ
linked to:
Google BigQuery
Google Cloud Platform ⓘ
linked to:
Google Cloud
Microsoft Azure ⓘ
linked to:
Azure
MySQL ⓘ PostgreSQL ⓘ Redshift ⓘ Snowflake ⓘ |
| license | Apache License 2.0 ⓘ |
| originalAuthor | Maxime Beauchemin ⓘ |
| programmingLanguage | Python ⓘ |
| repository | https://github.com/apache/airflow ⓘ |
| supports |
ETL workflows
ⓘ
Kubernetes deployment ⓘ cloud deployment ⓘ containerized deployment ⓘ cron-based scheduling ⓘ data engineering pipelines ⓘ data pipeline orchestration ⓘ dependency-based scheduling ⓘ directed acyclic graph workflows ⓘ event-driven workflows ⓘ machine learning pipelines ⓘ on-premises deployment ⓘ programmatic workflow authoring ⓘ time-based scheduling ⓘ workflow monitoring ⓘ workflow scheduling ⓘ |
| usesConcept | Directed Acyclic Graph ⓘ |
| website | https://airflow.apache.org ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Apache Airflow Description of subject: Apache Airflow is an open-source platform for programmatically authoring, scheduling, and monitoring complex workflows and data pipelines.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Apache Airflow