Snowflake is introducing a client connector for executing Apache Spark code directly within its cloud warehouse, eliminating the need for cluster setup. This solution addresses challenges faced by customers who find it burdensome to maintain separate systems for running Spark, typically written in Java, Python, or Scala. The new Snowpark Connector allows users to execute Spark code linked to Snowflake's analytics engine. Early users of this connector have reported performance improvements and significant cost savings compared to traditional Spark setups.
Customers have been using Spark for a long time to process data and get it ready for use in analytics or in AI. The burden of running in separate systems with different compute engines creates complexity in governance and infrastructure.
The feedback we got was it's often very hard to rewrite the type of transformations that people have built. Migrating Spark workloads due to the need to re-write code in Java, Python or Scala is often too much to contemplate.
With its new Snowpark Connector, Snowflake promises Spark users the ability to run Spark code in a Spark client, but linked to a Snowflake analytics engine rather than a separate Spark cluster.
Customers who've been running this in our pre-launch preview have seen an average of 5.6 times faster performance and about a 40 percent cost savings versus traditional Spark.
Collection
[
|
...
]