DataStream API
E711831
DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DataStream API canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T8093950 — 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: DataStream API Context triple: [Apache Flink, hasAPI, DataStream API]
-
A.
Apache Flink
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.
-
B.
Structured Streaming
Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
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.
-
E.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
- 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: DataStream API Target entity description: DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
-
A.
Apache Flink
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.
-
B.
Structured Streaming
Structured Streaming is Apache Spark’s scalable, fault-tolerant stream processing engine that lets developers express streaming computations using the same high-level APIs as batch processing.
-
C.
Kafka Streams
Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
-
D.
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.
-
E.
IBM Streams
IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
core abstraction in Apache Flink
ⓘ
stream processing API ⓘ |
| developedBy | Apache Flink community NERFINISHED ⓘ |
| domain |
big data
ⓘ
distributed systems ⓘ stream processing ⓘ |
| executionModel | continuous dataflow ⓘ |
| integratesWith |
Flink checkpointing mechanism
ⓘ
Flink runtime NERFINISHED ⓘ Flink savepoints NERFINISHED ⓘ |
| languageBinding |
Java
NERFINISHED
ⓘ
Python NERFINISHED ⓘ Scala NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| openSource | true ⓘ |
| partOf | Apache Flink NERFINISHED ⓘ |
| precedes | Flink Table API in abstraction level ⓘ |
| programmingModelFor | stateful stream processing ⓘ |
| provides |
access to keyed state
ⓘ
high-level stream transformations ⓘ low-level process functions ⓘ timers for event-time and processing-time callbacks ⓘ |
| supports |
aggregations
ⓘ
bounded data streams ⓘ connectors to external systems ⓘ event time processing ⓘ event-driven applications ⓘ event-time windows ⓘ exactly-once state consistency ⓘ fault tolerance via checkpoints ⓘ ingestion time semantics ⓘ keyed streams ⓘ non-keyed streams ⓘ processing time semantics ⓘ processing-time windows ⓘ session windows ⓘ side outputs ⓘ stateful operators ⓘ stream joins ⓘ transformations ⓘ unbounded data streams ⓘ watermarks ⓘ windowing ⓘ |
| targetUsers | stream processing application developers ⓘ |
| usedFor |
ETL pipelines
ⓘ
continuous data processing ⓘ event-driven microservices ⓘ real-time analytics ⓘ |
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: DataStream API Description of subject: DataStream API is Apache Flink’s core streaming abstraction for building stateful, event-driven data processing applications over unbounded and bounded data streams.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.