Apache Parquet
E457357
Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Apache Parquet canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4654943 — 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 Parquet Context triple: [Avro, competesWith, Apache Parquet]
-
A.
Apache ORC project
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.
-
B.
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.
-
C.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
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 Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop 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 Parquet Target entity description: Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
-
A.
Apache ORC project
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.
-
B.
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.
-
C.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
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 Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
- F. None of above. chosen
Statements (72)
| Predicate | Object |
|---|---|
| instanceOf |
columnar storage file format
ⓘ
open-source software project ⓘ |
| compressionCodec |
Brotli
NERFINISHED
ⓘ
Gzip NERFINISHED ⓘ LZ4 NERFINISHED ⓘ LZO ⓘ Snappy NERFINISHED ⓘ Zstandard NERFINISHED ⓘ |
| dataModel | columnar data model ⓘ |
| designedFor |
Hadoop ecosystem
NERFINISHED
ⓘ
distributed file systems ⓘ |
| developer | Apache Software Foundation NERFINISHED ⓘ |
| enables |
I/O reduction for analytical queries
ⓘ
efficient compression per column ⓘ vectorized execution in query engines ⓘ |
| feature |
column chunks
ⓘ
metadata footer ⓘ page-level encoding ⓘ per-column statistics ⓘ row groups ⓘ |
| fileExtension | .parquet ⓘ |
| fileFormatType |
binary
ⓘ
columnar ⓘ |
| governedBy | Apache Parquet community ⓘ |
| integratesWith |
Amazon S3
NERFINISHED
ⓘ
Azure Data Lake Storage NERFINISHED ⓘ Google Cloud Storage NERFINISHED ⓘ Hadoop Distributed File System NERFINISHED ⓘ cloud object storage ⓘ |
| license | Apache License 2.0 ⓘ |
| name | Apache Parquet NERFINISHED ⓘ |
| openSource | true ⓘ |
| optimizedFor |
analytical workloads
ⓘ
columnar processing ⓘ efficient data compression ⓘ query performance ⓘ |
| primaryUse |
big data analytics
ⓘ
data lake storage ⓘ data warehousing ⓘ |
| stores | data by column ⓘ |
| supports |
column pruning
ⓘ
complex data types ⓘ compression codecs ⓘ encoding schemes ⓘ nested data structures ⓘ predicate pushdown ⓘ schema evolution ⓘ statistics per column chunk ⓘ |
| supportsType |
BINARY
ⓘ
BOOLEAN ⓘ DECIMAL ⓘ DOUBLE ⓘ FLOAT ⓘ INT32 ⓘ INT64 ⓘ LIST ⓘ MAP ⓘ STRUCT ⓘ TIMESTAMP ⓘ |
| usedWith |
Amazon Athena
NERFINISHED
ⓘ
Apache Arrow NERFINISHED ⓘ Apache Drill NERFINISHED ⓘ Apache Flink NERFINISHED ⓘ Apache Hadoop NERFINISHED ⓘ Apache Hive NERFINISHED ⓘ Apache Impala NERFINISHED ⓘ Apache Spark NERFINISHED ⓘ Databricks NERFINISHED ⓘ Google BigQuery NERFINISHED ⓘ Presto NERFINISHED ⓘ Snowflake 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 Parquet Description of subject: Apache Parquet is a columnar storage file format optimized for efficient data compression and query performance in big data processing frameworks such as Apache Hadoop and Apache Spark.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.