Looking at the combination of SQL and Python, Snowflake can be classified as follows:
Posted: Mon Jan 27, 2025 4:50 am
An implementation example: The Snowpark API (Python)
Snowflake is a virtually tuning-free and maintenance-free data platform that is provided as software-as-a-service (SaaS) and is not based on well-known database technologies such as PostgreSQL or big data software platforms such as Hadoop . Instead, it combines a completely newly developed SQL query engine [12] with a cloud-native architecture and offers the possibility of implementing and operating both modern data warehouse implementations and data lake or machine learning use cases and data-intensive applications with a single platform. From a user perspective, it provides the functions of an analytical database required for company-wide use as well as numerous serverless features that help simplify data management overall.
SQL support: By design, Snowflake is 100 percent SQL-enabled and offers comprehensive support for ANSI SQL, ACID transactions, and SQL algeria telegram screening extensions for traversing, flattening, and handling semi-structured data.
Extensibility Framework: Snowpark is introduced as an extension to create a polyglot platform to bridge the gap between data engineers, data scientists, application developers, and the MLOps and DevOps teams that support them [13] . At its core, Snowpark is about allowing everyone to use the programming language and tools of their choice, while retaining familiar programming constructs such as dataframes.
Looking closer, the Snowpark library provides a data query and processing API for Scala, Python and Java to process the data in-place in the cloud storage layer managed by Snowflake using its own SQL compute engine.
Snowflake is a virtually tuning-free and maintenance-free data platform that is provided as software-as-a-service (SaaS) and is not based on well-known database technologies such as PostgreSQL or big data software platforms such as Hadoop . Instead, it combines a completely newly developed SQL query engine [12] with a cloud-native architecture and offers the possibility of implementing and operating both modern data warehouse implementations and data lake or machine learning use cases and data-intensive applications with a single platform. From a user perspective, it provides the functions of an analytical database required for company-wide use as well as numerous serverless features that help simplify data management overall.
SQL support: By design, Snowflake is 100 percent SQL-enabled and offers comprehensive support for ANSI SQL, ACID transactions, and SQL algeria telegram screening extensions for traversing, flattening, and handling semi-structured data.
Extensibility Framework: Snowpark is introduced as an extension to create a polyglot platform to bridge the gap between data engineers, data scientists, application developers, and the MLOps and DevOps teams that support them [13] . At its core, Snowpark is about allowing everyone to use the programming language and tools of their choice, while retaining familiar programming constructs such as dataframes.
Looking closer, the Snowpark library provides a data query and processing API for Scala, Python and Java to process the data in-place in the cloud storage layer managed by Snowflake using its own SQL compute engine.