Snowpark Connect enables Apache Spark code execution directly within Snowflake without requiring separate Spark clusters, solving issues related to data movement, costs, latency, and governance. It functions with Apache Iceberg tables and supports existing Spark code formats, delivering significant performance improvements—5.6 times faster processing—along with considerable cost savings of 41 percent. By utilizing Snowflake’s elastic compute runtime, organizations can benefit from automatic performance tuning while managing dependencies and version compatibility with ease, leveraging the advantages of Spark 3.4's client-server model.
Snowpark Connect facilitates Apache Spark code execution directly within Snowflake warehouses, eliminating the need for separate Spark clusters and associated complexities like data movement.
With Snowpark Connect, organizations experience 5.6 times faster performance and 41% cost savings compared to traditional managed Spark environments.
The architecture of Snowpark Connect leverages the separation of user code from the Spark cluster, which came with the introduction of Apache Spark 3.4, enhancing ease of use.
Snowpark Connect allows organizations to utilize modern Spark features while maintaining existing code, benefitting from Snowflake's elastic compute for automatic performance tuning.
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