Spark versus Snowflake?

Summary

• Apache Spark and Snowflake are different kinds of products. Spark is an open-source engine for data engineering, data science and machine learning that you run on infrastructure you choose; Snowflake is a cloud data platform that Snowflake operates for you.
• Spark has no storage layer of its own. It reads and writes Parquet, ORC, JSON, CSV and other sources, plus Delta Lake and Apache Iceberg tables, in storage you control, with APIs in Python, Scala, Java and SQL.
• Snowflake reorganizes loaded data into its own internally optimized, compressed, columnar format and runs queries and DML on virtual warehouses. Snowpark adds libraries for Java, Python and Scala.
• The two connect: the Snowflake Connector for Spark lets Spark read from and write to Snowflake, and Snowpark Connect for Apache Spark runs Spark DataFrame and Spark SQL code on Snowflake's compute engine.
• Choose on operating model, data location and format, team languages and workload mix, then test representative jobs. This page makes no performance or price comparisons.

Spark versus Snowflake?

They start from opposite ends. Apache Spark is an open-source processing engine, licensed under Apache 2.0, that you deploy on Standalone, Hadoop YARN or Kubernetes, or use through a managed service. It brings compute and APIs, and you choose the storage and the table format. Snowflake is a managed cloud data platform: "There is no hardware (virtual or physical) for you to select, install, configure, or manage," and data loaded into Snowflake tables is stored in Snowflake's own columnar format. Spark suits teams that want code-first pipelines across batch, streaming and machine learning over open formats in their own storage. Snowflake suits teams that want Snowflake to run storage, compute and services for them.

What each one is

Apache Spark is "a unified analytics engine for large-scale data processing," with APIs in Java, Scala, Python and R and higher-level tools including Spark SQL, pandas API on Spark, MLlib, GraphX and Structured Streaming. SparkR, the R API, "is deprecated from Apache Spark 4.0.0 and will be removed in a future version." Since Spark 3.4, Spark Connect has offered a client-server architecture that decouples client applications from Spark clusters. As of October 2026, the current release line is Spark 4.2.
Snowflake describes three layers: database storage, compute through virtual warehouses, and cloud services that coordinate activity across the platform. It runs on AWS, Microsoft Azure and Google Cloud.

Apache Spark vs Snowflake: how they compare

Capability	Apache Spark	Snowflake
Type of product	Open-source engine (Apache License 2.0) for work "on single-node machines or clusters"	Cloud data platform operated by Snowflake on AWS, Microsoft Azure and Google Cloud
Deployment	Standalone, Hadoop YARN or Kubernetes, or a managed Spark service	No hardware or software for you to select, install, configure or manage
Storage	None of its own; reads and writes Parquet, ORC, JSON, CSV, Avro and JDBC sources, and Delta Lake or Iceberg tables through each project's Spark connector	Loaded data goes into Snowflake's internally optimized, compressed, columnar format; Iceberg tables keep files in Snowflake storage or in external cloud storage you manage
Compute	A driver and executors you size; dynamic allocation can give idle executors back to the cluster	Virtual warehouses that can be resized while running, with auto-suspend, auto-resume and multi-cluster warehouses
Languages	Python, Scala, Java and SQL (SparkR deprecated)	SQL, plus Snowpark libraries for Java, Python and Scala
Streaming and incremental work	Structured Streaming, "built on the Spark SQL engine"	Snowpipe Streaming for row-level ingestion; dynamic tables that keep query results up to date
Machine learning	MLlib	Snowflake ML, "an integrated set of capabilities for end-to-end machine learning"
Running Spark code	Native, including remote Spark Connect clients	Snowpark Connect for Apache Spark runs Spark workloads on the Snowflake compute engine

How to choose

• Operating model. Who runs the compute: your team or a managed Spark service, or Snowflake.
• Data location and format. Spark works over files and open table formats in your storage. Snowflake tables use Snowflake's format, and its Iceberg tables can keep data in external storage you manage, with Snowflake or an external catalog as the Iceberg catalog.
• Languages and code portability. Spark DataFrame and SQL code is written against Apache Spark's open-source APIs. Snowflake offers SQL and Snowpark, and Snowpark Connect for Apache Spark supports the Spark DataFrame API and Spark SQL.
• Workload mix. Spark covers batch, streaming, graph processing and machine learning in one engine. Snowflake covers similar ground with warehouses plus Snowpipe Streaming, dynamic tables and Snowflake ML.

You don't always have to pick one. The Snowflake Connector for Spark "brings Snowflake into the Apache Spark ecosystem, enabling Spark to read data from, and write data to, Snowflake," and its 3.x line supports Spark 3.2 through 4.1. Iceberg tables give both engines a format they can share.

How Databricks approaches Spark and SQL analytics

Databricks runs Apache Spark as a managed platform: "Apache Spark is the technology powering compute clusters and SQL warehouses in Databricks." With serverless compute, Databricks allocates and manages compute for notebooks, jobs and Lakeflow pipelines, and Photon, the Databricks-native vectorized engine, runs supported Spark API operations without code changes. SQL warehouses serve SQL analytics on the same tables. Unity Catalog governs Delta Lake and Apache Iceberg tables, and its Iceberg REST catalog lets external Iceberg clients, including Apache Spark, Apache Flink, Trino and Snowflake, access Databricks tables with credential vending.

FAQs

Is Spark a data warehouse?

No. Spark is a processing engine without its own storage layer. Pair it with a table format such as Delta Lake or Apache Iceberg for ACID tables, and with a catalog for governance.

Can Snowflake run Spark code?

Yes. Snowpark Connect for Apache Spark lets you "connect your existing Spark workloads directly to Snowflake and run them on the Snowflake compute engine," using Spark Connect's client-server architecture.

Can Spark read data stored in Snowflake?

Yes, through the Snowflake Connector for Spark, which supports both reads and writes.