Snowpark DataFrame API
E431017
The Snowpark DataFrame API is a developer framework for building and executing scalable, DataFrame-style data transformations and applications directly within the Snowflake data platform.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Snowpark DataFrame API canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4326282 — 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.
Target entity: Snowpark DataFrame API Context triple: [Python (via Snowpark), uses, Snowpark DataFrame API]
-
A.
Python (via Snowpark)
Python (via Snowpark) is Snowflake’s integration of the Python language for building and running data pipelines, machine learning, and other data applications directly within the Snowflake data platform.
-
B.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
C.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
D.
Java (via Snowpark)
Java (via Snowpark) is the capability within Snowflake’s Snowpark framework that lets developers write and execute data processing and analytics logic in Java directly inside the Snowflake data platform.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Target entity: Snowpark DataFrame API Target entity description: The Snowpark DataFrame API is a developer framework for building and executing scalable, DataFrame-style data transformations and applications directly within the Snowflake data platform.
-
A.
Python (via Snowpark)
Python (via Snowpark) is Snowflake’s integration of the Python language for building and running data pipelines, machine learning, and other data applications directly within the Snowflake data platform.
-
B.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
C.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
D.
Java (via Snowpark)
Java (via Snowpark) is the capability within Snowflake’s Snowpark framework that lets developers write and execute data processing and analytics logic in Java directly inside the Snowflake data platform.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
Statements (49)
| Predicate | Object |
|---|---|
| instanceOf |
Snowflake feature
ⓘ
data processing API ⓘ developer framework ⓘ |
| avoids | data movement out of Snowflake ⓘ |
| designedFor |
ETL and ELT workloads
ⓘ
data applications ⓘ data engineering ⓘ data science ⓘ machine learning pipelines ⓘ |
| developedBy | Snowflake Inc. NERFINISHED ⓘ |
| documentationURL | https://docs.snowflake.com/en/developer-guide/snowpark ⓘ |
| enables |
DataFrame-style query construction
ⓘ
server-side data processing in Snowflake ⓘ type-safe query building ⓘ use of familiar programming languages for data transformations ⓘ |
| executionLocation | inside Snowflake compute ⓘ |
| executionModel | pushdown to Snowflake engine ⓘ |
| integratesWith |
Snowflake security model
ⓘ
Snowflake stored procedures ⓘ Snowflake user-defined functions ⓘ Snowflake warehouses ⓘ |
| marketedAs | Snowpark DataFrame API NERFINISHED ⓘ |
| partOf |
Snowflake Data Cloud
NERFINISHED
ⓘ
Snowpark NERFINISHED ⓘ |
| primaryAbstraction | DataFrame ⓘ |
| runsOn | Snowflake platform NERFINISHED ⓘ |
| supportsFeature |
aggregations
ⓘ
dataframe caching semantics (logical) ⓘ filtering ⓘ joins ⓘ parameterized queries ⓘ projections ⓘ session management ⓘ sorting ⓘ stored procedure invocation ⓘ table functions ⓘ user-defined functions ⓘ window functions ⓘ |
| supportsParadigm |
declarative data transformations
ⓘ
functional-style transformations ⓘ lazy evaluation ⓘ |
| supportsProgrammingLanguage |
Java
NERFINISHED
ⓘ
JavaScript NERFINISHED ⓘ Python NERFINISHED ⓘ SQL NERFINISHED ⓘ Scala NERFINISHED ⓘ |
| targetUser |
application developers
ⓘ
data engineers ⓘ data scientists ⓘ |
How these facts were elicited
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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.
Subject: Snowpark DataFrame API Description of subject: The Snowpark DataFrame API is a developer framework for building and executing scalable, DataFrame-style data transformations and applications directly within the Snowflake data platform.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.