Apache ORC project
E426165
The Apache ORC project is an open-source initiative that develops the Optimized Row Columnar (ORC) file format for efficient, high-performance storage and processing of large-scale data in big data ecosystems.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Apache ORC project canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4280251 — 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 ORC project Context triple: [ORC, partOf, Apache ORC project]
-
A.
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.
-
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 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.
-
D.
Apache HBase
Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
-
E.
Apache Sqoop
Apache Sqoop is an open-source tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
- 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 ORC project Target entity description: The Apache ORC project is an open-source initiative that develops the Optimized Row Columnar (ORC) file format for efficient, high-performance storage and processing of large-scale data in big data ecosystems.
-
A.
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.
-
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 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.
-
D.
Apache HBase
Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
-
E.
Apache Sqoop
Apache Sqoop is an open-source tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
- F. None of above. chosen
Statements (57)
| Predicate | Object |
|---|---|
| instanceOf |
Apache Software Foundation project
ⓘ
columnar storage format project ⓘ open-source software project ⓘ |
| abbreviation | ORC NERFINISHED ⓘ |
| category |
big data file format
ⓘ
data storage technology ⓘ |
| compatibleWith | Hadoop Distributed File System NERFINISHED ⓘ |
| dataModel | self-describing schema ⓘ |
| defines | Optimized Row Columnar (ORC) file format NERFINISHED ⓘ |
| developer | Apache Software Foundation NERFINISHED ⓘ |
| domain |
big data
ⓘ
data warehousing ⓘ distributed data processing ⓘ |
| fileFormatType | columnar file format ⓘ |
| fullName | Apache Optimized Row Columnar NERFINISHED ⓘ |
| goal |
enable efficient compression
ⓘ
improve query performance ⓘ reduce storage footprint ⓘ |
| governedBy | Apache Software Foundation NERFINISHED ⓘ |
| hasComponent |
data streams
ⓘ
footer metadata ⓘ index data ⓘ row groups ⓘ stripes ⓘ |
| license | Apache License 2.0 ⓘ |
| name | Apache ORC NERFINISHED ⓘ |
| optimizedFor |
large-scale batch processing
ⓘ
read-heavy analytical workloads ⓘ |
| primaryOutput | ORC file format ⓘ |
| programmingLanguage |
C++
ⓘ
Java ⓘ |
| purpose |
efficient storage of large-scale data
ⓘ
high-performance data processing ⓘ |
| repository | https://orc.apache.org/ ⓘ |
| sourceRepository | https://github.com/apache/orc ⓘ |
| stores |
nested data structures
ⓘ
table data ⓘ |
| supportsDataType |
complex types
ⓘ
list ⓘ map ⓘ primitive types ⓘ struct ⓘ union ⓘ |
| supportsFeature |
ACID table support in some engines
ⓘ
columnar storage ⓘ compression ⓘ predicate pushdown ⓘ schema evolution ⓘ splittable files ⓘ statistics per stripe and row group ⓘ type evolution ⓘ |
| usedIn |
Apache Flink
NERFINISHED
ⓘ
Apache Hadoop ecosystem NERFINISHED ⓘ Apache Hive NERFINISHED ⓘ Apache Spark NERFINISHED ⓘ Presto NERFINISHED ⓘ Trino NERFINISHED ⓘ |
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 ORC project Description of subject: The Apache ORC project is an open-source initiative that develops the Optimized Row Columnar (ORC) file format for efficient, high-performance storage and processing of large-scale data in big data ecosystems.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.