Amazon EMR
E459746
Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
All labels observed (6)
| Label | Occurrences |
|---|---|
| Amazon EMR canonical | 9 |
| AWS EMR | 1 |
| Amazon EMR Serverless | 1 |
| Amazon EMR on EC2 | 1 |
| Amazon EMR on EKS | 1 |
| Amazon Elastic MapReduce | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4600409 — 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: Amazon EMR Context triple: [Amazon S3, integratesWith, Amazon EMR]
-
A.
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.
-
B.
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.
-
C.
Amazon Kinesis
Amazon Kinesis is a fully managed AWS service for real-time collection, processing, and analysis of streaming data at scale.
-
D.
Amazon Athena
Amazon Athena is a serverless, interactive query service from AWS that lets users analyze data directly in Amazon S3 using standard SQL.
-
E.
Google Cloud Dataproc
Google Cloud Dataproc is a managed cloud service for running Apache Hadoop, Spark, and other big data workloads on scalable, automated clusters in Google Cloud.
- 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: Amazon EMR Target entity description: Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
-
A.
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.
-
B.
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.
-
C.
Amazon Kinesis
Amazon Kinesis is a fully managed AWS service for real-time collection, processing, and analysis of streaming data at scale.
-
D.
Amazon Athena
Amazon Athena is a serverless, interactive query service from AWS that lets users analyze data directly in Amazon S3 using standard SQL.
-
E.
Google Cloud Dataproc
Google Cloud Dataproc is a managed cloud service for running Apache Hadoop, Spark, and other big data workloads on scalable, automated clusters in Google Cloud.
- F. None of above. chosen
Statements (61)
| Predicate | Object |
|---|---|
| instanceOf |
AWS service
ⓘ
cloud service ⓘ managed big data platform ⓘ |
| abbreviationOf |
Amazon Elastic MapReduce
ⓘ
linked to:
Amazon EMR
|
| deploymentModel |
Amazon EMR Serverless
ⓘ
linked to:
Amazon EMR
Amazon EMR on EC2 ⓘ
linked to:
Amazon EMR
Amazon EMR on EKS ⓘ
linked to:
Amazon EMR
|
| developedBy | Amazon Web Services ⓘ |
| feature |
EMR File System
ⓘ
EMR Notebooks ⓘ automatic cluster scaling ⓘ bootstrap actions ⓘ data encryption at rest ⓘ data encryption in transit ⓘ job scheduling ⓘ managed cluster provisioning ⓘ managed software configuration ⓘ security group integration ⓘ spot instance support ⓘ |
| hostPlatform | Amazon Web Services ⓘ |
| integratesWith |
AWS Glue
ⓘ
AWS Identity and Access Management ⓘ AWS Key Management Service ⓘ AWS Lake Formation ⓘ Amazon CloudWatch ⓘ Amazon DynamoDB ⓘ Amazon Redshift ⓘ Amazon S3 ⓘ Amazon VPC ⓘ |
| partOf | Amazon Web Services portfolio ⓘ |
| pricingModel | pay as you go ⓘ |
| primaryPurpose |
big data analytics
ⓘ
large scale data processing ⓘ |
| providedBy | Amazon Web Services ⓘ |
| regionAvailability | multiple AWS regions worldwide ⓘ |
| runsOn | Amazon EC2 ⓘ |
| supportsFramework |
Apache Flink
ⓘ
Apache HBase ⓘ Apache Hadoop ⓘ
linked to:
Hadoop
Apache Hive ⓘ Apache Livy ⓘ Apache Pig ⓘ Apache Spark ⓘ Apache Tez ⓘ Presto ⓘ Trino ⓘ |
| supportsLanguage |
Java
ⓘ
Python ⓘ R ⓘ SQL ⓘ Scala ⓘ |
| supportsStorage |
Amazon S3
ⓘ
EMR File System ⓘ HDFS ⓘ local instance storage ⓘ |
| supportsUseCase |
ETL workloads
ⓘ
batch data processing ⓘ data warehousing ⓘ interactive analytics ⓘ log processing ⓘ machine learning preprocessing ⓘ |
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: Amazon EMR Description of subject: Amazon EMR is a managed big data platform on AWS that simplifies running large-scale data processing frameworks like Apache Hadoop and Spark on elastic cloud clusters.
Referenced by (14)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Amazon EMR
linked to: Amazon EMR
linked to: Amazon EMR
linked to: Amazon EMR
linked to: Amazon EMR