Apache Flink
E188935
big data framework
distributed data processing framework
open-source software
stream processing framework
Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Apache Flink canonical | 7 |
How this entity was disambiguated
This entity first appeared as the object of triple T1647851 — 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 Flink Context triple: [Hadoop, influenced, Apache Flink]
-
A.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
B.
Apache Storm
Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
-
C.
Apache Flume
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log and event data into Hadoop and other data stores.
-
D.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
E.
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.
- 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 Flink Target entity description: Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
-
A.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
B.
Apache Storm
Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
-
C.
Apache Flume
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log and event data into Hadoop and other data stores.
-
D.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
E.
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.
- F. None of above. chosen
Statements (69)
| Predicate | Object |
|---|---|
| instanceOf |
big data framework
ⓘ
distributed data processing framework ⓘ open-source software ⓘ stream processing framework ⓘ |
| designedFor |
high-throughput workloads
ⓘ
large-scale data processing ⓘ low-latency workloads ⓘ |
| developer | Apache Software Foundation ⓘ |
| hasAPI |
DataSet API
ⓘ
DataStream API ⓘ SQL API ⓘ Table API ⓘ |
| hasComponent |
Dispatcher
ⓘ
JobManager ⓘ ResourceManager ⓘ TaskManager ⓘ |
| integratesWith |
Amazon Kinesis
ⓘ
Apache HBase ⓘ Hadoop ⓘ
surface form:
Apache Hadoop
Apache Hive ⓘ Apache Kafka ⓘ JDBC data sources ⓘ |
| license | Apache License 2.0 ⓘ |
| partOf | Apache ecosystem ⓘ |
| programmingLanguage |
Java
ⓘ
Scala ⓘ |
| repository | https://github.com/apache/flink ⓘ |
| supportsDeployment |
Kubernetes
ⓘ
Mesos ⓘ YARN ⓘ cloud environments ⓘ native Kubernetes deployment ⓘ standalone cluster ⓘ |
| supportsFeature |
SQL-based analytics
ⓘ
batch table API ⓘ checkpointing ⓘ complex event processing ⓘ connectors to external systems ⓘ data stream API ⓘ event-time windows ⓘ exactly-once state consistency ⓘ fault tolerance ⓘ high-throughput processing ⓘ iterative processing ⓘ low-latency processing ⓘ savepoints ⓘ state backends ⓘ stateful functions ⓘ streaming table API ⓘ table API ⓘ watermarks ⓘ |
| supportsLanguage |
Java
ⓘ
Python ⓘ SQL ⓘ Scala ⓘ |
| supportsModel |
batch processing
ⓘ
event-time processing ⓘ stateful stream processing ⓘ stream processing ⓘ |
| supportsSemantic |
at-least-once
ⓘ
exactly-once ⓘ |
| useCase |
ETL pipelines
ⓘ
event-driven applications ⓘ fraud detection ⓘ log processing ⓘ machine learning pipelines ⓘ monitoring and alerting ⓘ real-time analytics ⓘ |
| website | https://flink.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 Flink Description of subject: Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.