Running Spark is not just about writing transformations and business logic. It also means tuning clusters, patching infrastructure and managing dependency environments.
Moving those workloads onto Snowflake addresses that directly, with customers experiencing up to 5.1x faster performance and 42% lower costs1. Snowpark Connect for Apache Spark™ lets your existing Spark code run on Snowflake's engine with minimal changes; no clusters to provision, tune or patch.
Most generic AI coding assistants can fall short when it comes to migration at scale. They can rewrite a snippet, but they lack the compatibility context to move an entire codebase to a new engine: which patterns are unsupported, how to map them to DataFrame equivalents and how to record what changed. The spark-migration skill in Snowflake CoCo, a data-native AI coding agent, is built to close that gap.
One prompt, one codebase
The clearest way to see the skill is to watch it run. The demo below starts with a directory of data pipelines written in PySpark.
