Snowflake Inc. Launches Public Preview of Snowpark Connect for Spark, Improving Performance and Cost Efficiency.
PorAinvest
sábado, 23 de agosto de 2025, 3:34 am ET1 min de lectura
SNOW--
Snowpark Connect is designed to offer significant performance improvements and cost savings compared to managed Spark services. According to Snowflake Inc., the new architecture delivers up to 5.6x faster performance and 41% cost savings. The initial release supports Python and Spark 3.5.x, with plans to introduce support for Java and Scala in the future.
The Snowflake Connector for Python, available on PyPI, has seen numerous updates and improvements. Recent releases have included bug fixes, new features, and enhanced security measures. For instance, version 3.17.2 fixed a bug related to platform detection and added a new parameter to disable endpoint-based platform detection [1].
The integration of Snowpark Connect with Snowflake's robust data warehousing capabilities is expected to streamline data processing workflows and enhance overall operational efficiency. As Snowflake continues to innovate in the data warehousing space, this latest development positions the company as a leader in providing high-performance, scalable, and cost-effective data solutions.
References:
[1] https://pypi.org/project/snowflake-connector-python/
Snowflake Inc. has launched the public preview of Snowpark Connect for Spark, allowing Apache Spark code to run directly in Snowflake warehouses. This new architecture addresses issues with Spark Connector, including costly data movement, latency, and governance issues. Snowflake Inc. claims that this launch delivers up to 5.6x faster performance and 41% cost savings compared to managed Spark services. Snowpark Connect currently supports Python and Spark 3.5.x, with Java and Scala support planned for the future.
Snowflake Inc. has announced the public preview of Snowpark Connect for Spark, a new architecture that enables Apache Spark code to run directly within Snowflake warehouses. This innovative solution aims to address several challenges associated with traditional Spark Connector, such as high data movement costs, latency issues, and governance concerns.Snowpark Connect is designed to offer significant performance improvements and cost savings compared to managed Spark services. According to Snowflake Inc., the new architecture delivers up to 5.6x faster performance and 41% cost savings. The initial release supports Python and Spark 3.5.x, with plans to introduce support for Java and Scala in the future.
The Snowflake Connector for Python, available on PyPI, has seen numerous updates and improvements. Recent releases have included bug fixes, new features, and enhanced security measures. For instance, version 3.17.2 fixed a bug related to platform detection and added a new parameter to disable endpoint-based platform detection [1].
The integration of Snowpark Connect with Snowflake's robust data warehousing capabilities is expected to streamline data processing workflows and enhance overall operational efficiency. As Snowflake continues to innovate in the data warehousing space, this latest development positions the company as a leader in providing high-performance, scalable, and cost-effective data solutions.
References:
[1] https://pypi.org/project/snowflake-connector-python/
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