Apache Hive
E185675
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.
All labels observed (5)
| Label | Occurrences |
|---|---|
| Apache Hive canonical | 31 |
| Hive | 4 |
| Apache Hive distribution | 1 |
| Apache Hive ecosystem | 1 |
| Hive execution engine | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1647853 — 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 Hive Context triple: [Hadoop, ecosystemIncludes, Apache Hive]
-
A.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
B.
Amazon Redshift
Amazon Redshift is a fully managed, cloud-based data warehousing service from Amazon Web Services designed for fast querying and analysis of large datasets using SQL.
-
C.
AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
-
D.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
E.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
- 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 Hive Target entity description: 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.
-
A.
Hadoop
Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
-
B.
Amazon Redshift
Amazon Redshift is a fully managed, cloud-based data warehousing service from Amazon Web Services designed for fast querying and analysis of large datasets using SQL.
-
C.
AWS Glue
AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
-
D.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
E.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
- F. None of above. chosen
Statements (62)
| Predicate | Object |
|---|---|
| instanceOf |
SQL query engine
ⓘ
data warehouse software ⓘ open-source software ⓘ |
| developer |
Apache Software Foundation
ⓘ
Facebook ⓘ |
| domain |
big data analytics
ⓘ
distributed data processing ⓘ |
| donatedTo | Apache Software Foundation ⓘ |
| donationYear | 2008 ⓘ |
| feature |
ACID transactions
ⓘ
JDBC driver ⓘ ODBC driver ⓘ bucketing ⓘ cost-based optimizer ⓘ materialized views ⓘ metastore ⓘ partitioning ⓘ schema-on-read ⓘ user-defined functions ⓘ vectorized query execution ⓘ |
| inception | 2007 ⓘ |
| introducedBy | Facebook Data Infrastructure Team ⓘ |
| license | Apache License 2.0 ⓘ |
| operatingSystem | cross-platform ⓘ |
| partOf |
Apache Hadoop ecosystem
ⓘ
linked to:
Hadoop
|
| primaryUse |
ETL processing
ⓘ
batch processing of large datasets ⓘ data warehousing ⓘ |
| programmingLanguage |
C++
ⓘ
Java ⓘ Python ⓘ |
| repository | https://github.com/apache/hive ⓘ |
| runsOn |
Apache Spark
ⓘ
Apache Tez ⓘ Hadoop YARN ⓘ
linked to:
YARN
|
| supportsConcept |
UDAF
ⓘ
UDF ⓘ UDTF ⓘ external tables ⓘ indexes ⓘ managed tables ⓘ views ⓘ |
| supportsFileFormat |
Avro
ⓘ
JSON ⓘ ORC ⓘ Parquet ⓘ RCFile ⓘ SequenceFile ⓘ Text ⓘ |
| supportsLanguage |
HiveQL
ⓘ
SQL-like query language ⓘ |
| supportsPlatform |
Amazon S3
ⓘ
Azure Data Lake Storage ⓘ Google Cloud Storage ⓘ Hadoop Distributed File System ⓘ
linked to:
HDFS
|
| topLevelProjectOf | Apache Software Foundation ⓘ |
| usesComponent |
Beeline
ⓘ
CLI shell ⓘ Hive Metastore ⓘ HiveServer2 ⓘ |
| website | https://hive.apache.org/ ⓘ |
| writtenIn | Java ⓘ |
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 Hive Description of subject: 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.
Referenced by (38)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Apache Hive
linked to: Apache Hive
subject linked to:
Apache ORC project
linked to: Apache Hive
linked to: Apache Hive
linked to: Apache Hive
linked to: Apache Hive