Spark to SNowpark for Data Engineering

Migrate Spark pipelines with minimal code changes and reduce operational overhead with an elastic processing engine that natively supports Python, Java, and Scala.

Two project leads standing and looking at a tablet

Code like Pyspark. Execute Faster.

Develop data transformations and custom business logic from your integrated development environment or notebook of choice using the Snowpark API. Securely execute code in Snowflake’s compute runtimes for elastic, performant and governed processing.

Digram showing how users can develop code from any IDE with the Snowpark API

Spark to Snowpark Overview

Dataframe API

Build queries using Spark-like DataFrames from your integrated development environment of choice and push down processing to Snowflake’s elastic processing engine.

User-Defined Functions

Run custom logic written in Python or Java to run directly in Snowflake using User Defined Functions (UDFs) that can be migrated from Spark with minimal code changes.

Stored Procedures

Operationalize and orchestrate your pipelines and custom logic directly inside Snowflake and make it accessible to your SQL users.

Snowflake Platform for

Multiple Languages

Snowflake’s unique multi-cluster shared data architecture powers the performance, elasticity, and governance of Snowpark.

Hear From Snowpark Developers

Customers are migrating from Spark to Snowpark for scalable, governed data pipelines.

Minimal Code Changes

“We wanted to switch to Snowpark for performance reasons and it was so easy to do. Converting our PySpark code to Snowpark was as simple as a change in an import statement.”

Principal Data Engineer, Homegenius

Better Price-Performance

“Before, we had to move the data for processing with other languages and then bring results back to make those accessible. Now with Snowpark, we are bringing the processing to the data, streamlining our architecture and making our data engineering pipelines and intelligent applications more cost effective with processing happening within Snowflake, our one single platform.”

Sr. Director Clinical Data Analytics, IQVIA

Less Operations Overhead

“With our previous Spark-based platforms, there came a point where it would be difficult to scale, and we were missing our load SLAs. With Snowflake, the split between compute and storage makes it much easier. We haven’t missed an SLA since migrating.”

Senior Manager of Data Platforms, EDF

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