Databricks
E313462
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
All labels observed (6)
| Label | Occurrences |
|---|---|
| Azure Databricks | 5 |
| Databricks canonical | 1 |
| Databricks Data Engineering | 1 |
| Databricks Lakehouse Platform | 1 |
| Databricks Machine Learning | 1 |
| Databricks SQL | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T2956209 — 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: Databricks Context triple: [United States technology sector, majorCompany, Databricks]
-
A.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
B.
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based analytics service from Microsoft that unifies big data and data warehousing to enable large-scale data integration, exploration, and business intelligence.
-
C.
Azure Data Lake Storage
Azure Data Lake Storage is a scalable, secure cloud-based data lake service from Microsoft designed for big data analytics and enterprise data warehousing workloads.
-
D.
Azure Data Factory
Azure Data Factory is a cloud-based data integration service from Microsoft that enables users to create, schedule, and orchestrate data pipelines for moving and transforming data at scale across diverse sources.
-
E.
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.
- 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: Databricks Target entity description: Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
A.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
B.
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based analytics service from Microsoft that unifies big data and data warehousing to enable large-scale data integration, exploration, and business intelligence.
-
C.
Azure Data Lake Storage
Azure Data Lake Storage is a scalable, secure cloud-based data lake service from Microsoft designed for big data analytics and enterprise data warehousing workloads.
-
D.
Azure Data Factory
Azure Data Factory is a cloud-based data integration service from Microsoft that enables users to create, schedule, and orchestrate data pipelines for moving and transforming data at scale across diverse sources.
-
E.
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.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
Cloud computing company
ⓘ
Data and AI company ⓘ Private company ⓘ Technology company ⓘ |
| CEO | Ali Ghodsi ⓘ |
| coreTechnology | Apache Spark ⓘ |
| countryOfOrigin |
United States
ⓘ
linked to:
United States of America
|
| deploymentModel | Cloud-based ⓘ |
| foundedBy |
Ali Ghodsi
ⓘ
Andy Konwinski ⓘ Arsalan Tavakoli-Shiraji ⓘ Ion Stoica ⓘ Matei Zaharia ⓘ Patrick Wendell ⓘ Reynold Xin ⓘ |
| foundedIn | San Francisco ⓘ |
| foundedYear | 2013 ⓘ |
| hasCustomerSegment |
Data engineers
ⓘ
Data scientists ⓘ Enterprises ⓘ Machine learning engineers ⓘ |
| headquarteredIn |
California
ⓘ
linked to:
California, United States
San Francisco ⓘ United States ⓘ
linked to:
United States of America
|
| industry |
Artificial intelligence
ⓘ
Big data ⓘ Cloud computing ⓘ Data analytics ⓘ Data engineering ⓘ Machine learning ⓘ |
| integratesWith |
Amazon Web Services
ⓘ
Google Cloud Platform ⓘ
linked to:
Google Cloud
Microsoft Azure ⓘ
linked to:
Azure
|
| knownFor |
Lakehouse architecture
ⓘ
Unified analytics platform ⓘ |
| offersProduct |
Databricks Data Engineering
ⓘ
linked to:
Databricks
Databricks Lakehouse Platform ⓘ
linked to:
Databricks
Databricks Machine Learning ⓘ
linked to:
Databricks
Databricks SQL ⓘ
linked to:
Databricks
|
| provides |
Collaborative notebooks
ⓘ
Delta Lake storage layer ⓘ Managed Apache Spark clusters ⓘ |
| supportsLanguage |
Java
ⓘ
Python ⓘ R ⓘ SQL ⓘ Scala ⓘ |
| supportsWorkload |
Data engineering
ⓘ
Data science ⓘ Machine learning ⓘ Streaming 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: Databricks Description of subject: Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
Referenced by (10)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks
linked to: Databricks