Snowpark vs Databricks serverless - which is better?

Summary

• Neither is better in general. Snowpark is Snowflake's library for processing data inside Snowflake, and Databricks serverless is Databricks-managed compute for Spark workloads, so the choice follows where your data and governance live.
• Snowpark provides libraries for Java, Python and Scala, and its code runs on Snowflake virtual warehouses.
• Databricks serverless compute runs notebooks, jobs and Lakeflow Pipelines without you provisioning compute in your cloud account. Serverless notebooks do not support Scala or R.
• Each vendor documents a way across the boundary: Databricks can query Snowflake data through Lakehouse Federation, and Snowflake can run Spark DataFrame code through Snowpark Connect for Apache Spark.

Snowpark vs Databricks serverless - which is better?

Neither is better in general, because they are different kinds of things. Snowpark is a set of libraries for processing data inside Snowflake, and Databricks serverless is Databricks-managed compute that runs notebooks, jobs and Lakeflow Pipelines. Choose by where your data and governance already live, which API your code is written against, and which documented runtime limits matter to your workload.

What is Snowpark?

Snowpark is a library, available in Java, Python and Scala, for querying and processing data in Snowflake. Snowflake's docs say you can build applications that process data in Snowflake without moving data to the system where your application code runs, and that processing happens "as part of the elastic and serverless Snowflake engine." The core abstraction is the DataFrame, which is evaluated lazily on the server, and user-defined functions are pushed to the Snowflake engine. Snowflake's key concepts page adds that virtual warehouses process SQL statements and, using Snowpark, run code in languages such as Java, Python and Scala.

What is Databricks serverless compute?

Serverless compute is a Databricks-managed service that provides on-demand compute for notebooks, jobs and Lakeflow Pipelines. You run workloads without provisioning compute resources in your cloud account, and Databricks allocates and manages them. Serverless SQL warehouses, model serving and AI features also use serverless infrastructure, but they are configured separately.

Snowpark vs Databricks serverless: how they compare

Aspect	Snowpark	Databricks serverless compute
What it is	Libraries for Java, Python and Scala	Managed compute for notebooks, jobs and Lakeflow Pipelines
Where code runs	Snowflake virtual warehouses	Serverless compute plane managed by Databricks
Main API	Snowpark API with DataFrames, or the Spark DataFrame API through Snowpark Connect for Apache Spark	Spark through Spark Connect APIs; Spark RDD APIs are not supported
Languages	Java, Python, Scala	R is not supported; Scala is not supported in notebooks; JAR tasks run in jobs
Storage	Snowflake tables, Apache Iceberg tables, hybrid tables	Delta, Iceberg and other listed formats; Unity Catalog is required for external sources
Clouds	AWS, Google Cloud, Azure	AWS, Azure, Google Cloud
Scheduled transformation	Tasks, streams and dynamic tables	Lakeflow Jobs triggers and Lakeflow Pipelines

When does each one fit?

• Your data already lives in Snowflake and your team writes Snowpark. Snowpark processes data in Snowflake without moving it, so code stays next to the data.
• Your data and governance are in Unity Catalog, or your code is PySpark or Spark SQL. Serverless compute runs Spark code through Spark Connect, and your workspace must be enabled for Unity Catalog.
• You already have Spark code. Spark DataFrame code can target either platform. Snowpark Connect for Apache Spark runs it on a Snowflake warehouse, and Databricks serverless runs it through Spark Connect.
• Language support decides it. Check the limits first: R is not supported on serverless compute, and Scala is not supported in serverless notebooks.

How Databricks approaches serverless compute

Lakeflow Jobs can run tasks on serverless compute. Serverless is supported for the notebook, Python script, dbt, Python wheel and JAR task types, and autoscaling and Photon are enabled automatically. A Performance optimized setting chooses between standard mode, for workloads that tolerate higher startup latency, and a faster mode for time-sensitive workloads. Databricks recommends developing new pipelines on serverless compute.
Review the serverless limitations page before you move code. It documents a maximum runtime of seven days per run and no Spark UI, so you use the query profile instead.

Can you use both?

Yes. Databricks can query Snowflake data in place through Lakehouse Federation, which uses a connection and a foreign catalog in Unity Catalog. Query federation pushes the query down to Snowflake over JDBC. Catalog federation runs the Unity Catalog query directly against file storage, which the docs describe as helpful for incremental migration or as a longer-term hybrid model. Databricks Runtime also includes a Snowflake connector for reading and writing data, and Databricks recommends Lakehouse Federation when it supports your source.

FAQs

Can I run Spark code on Snowflake?

Yes. Snowpark Connect for Apache Spark connects existing Spark workloads to Snowflake and supports the Spark DataFrame API. All workloads run on a Snowflake warehouse, and Snowflake lists Python, Java, Scala and Spark SQL as supported languages.

Can Databricks serverless read Snowflake tables?

Yes. SNOWFLAKE is on the list of data sources that serverless compute supports for reads, and Lakehouse Federation exposes Snowflake data as a foreign catalog in Unity Catalog.

Does Databricks serverless support Scala and R?

Not in notebooks. The limitations page says Scala and R are not supported in notebooks, and R is not supported at all. JAR tasks in jobs are supported.

Is serverless compute the same as a serverless SQL warehouse?

No. Serverless compute covers notebooks, jobs and Lakeflow Pipelines. Serverless SQL warehouses use serverless infrastructure independently and have their own configuration path.

Which one should I choose?

Start from where the data and governance live and which API your code uses. Snowpark code over Snowflake data stays in Snowflake. Spark code that you want on Databricks runs on serverless compute through Spark Connect.