Snowflake's Snowpark Connect: Run Apache Spark Analytics in Cloud
Snowflake is advancing its capabilities by introducing Snowpark Connect for Apache Spark, a new offering that allows enterprises to run their Apache Spark analytics workloads directly within the Snowflake Data Cloud. This development aims to streamline data operations, reduce costs, and enhance performance by eliminating the need for separate Spark instances and the associated data transfer delays.
Historically, organizations using Snowflake with Spark often relied on the Snowflake Connector for Spark. This connector acts as a bridge, enabling data to move between Spark clusters and Snowflake. However, this approach could introduce latency and additional costs due to data movement across systems. Snowpark Connect for Apache Spark, currently in public preview and compatible with Spark 3.5.x, represents a significant shift. It leverages Spark Connect, a feature introduced in Apache Spark 3.4, which decouples user code from the Spark cluster. This means that applications, such as Python scripts or data notebooks, can send logical plans to a remote Spark cluster, with the heavy lifting and result processing handled by the cluster itself.
Snowflake's implementation of Spark Connect allows Spark code to run on Snowflake's vectorized engine directly within the Data Cloud. This provides Snowflake customers with the familiarity of Spark's APIs while leveraging Snowflake's optimized engine and serverless architecture. Sanjeev Mohan, chief analyst at SanjMo, highlights that this new capability will simplify the movement of Spark code to Snowpark, offering a combination of Spark's ease of use and Snowflake's inherent simplicity. Furthermore, it is expected to lower the total cost of ownership for enterprises by allowing developers to utilize Snowflake's serverless engine and avoid the complexities of Spark tuning.
Beyond cost savings, Snowpark Connect for Apache Spark promises faster processing due to Snowflake's vectorized engine. It also addresses challenges such as the difficulty in finding staff with specialized Spark expertise, as much of the operational overhead is managed by Snowflake. Shubham Yadav, a senior analyst at Everest Group, views this launch as timely, given the increasing adoption of AI and ML and the corresponding demand for simplified infrastructure and reduced costs.
It is crucial for enterprises to differentiate between the new Snowpark Connect for Apache Spark and the existing Snowflake Connector for Spark. While the Connector facilitates data transfer between Spark and Snowflake, Snowpark Connect effectively "relocates" the Spark processing into Snowflake, minimizing data movement and its associated latency and cost. Migrating from the older Connector to Snowpark Connect for Apache Spark is designed to be seamless, requiring no code conversion.
This move by Snowflake positions it more directly against rivals like Databricks, which offers similar capabilities through its Databricks Connect offering. While Snowflake has traditionally been optimized as a data warehouse for structured data and SQL-first workflows, Databricks, built on a lakehouse architecture, has excelled in handling structured and unstructured data, particularly for complex machine learning and streaming jobs. However, both platforms are continuously evolving, with increasing overlaps in their functionalities. Snowflake's Snowpark, with its DataFrame APIs and support for various languages like Python, Java, and Scala, is geared towards in-database processing, offering significant performance gains and cost savings over managed Spark environments. This allows developers to build data pipelines and applications directly within Snowflake, reducing data transfer and simplifying governance.