Snowflake has released a new AI-powered migration tool that converts Apache Spark code to run on Snowflake's database engine in a single prompt.
Customers using this approach report up to 5.1x faster performance and 42% lower costs compared to managed Spark services, while avoiding the operational overhead of cluster management.
The tool is included with Snowflake's CoCo coding agent and works with Python, Scala, and Java code.
What happened
Snowflake announced a new spark-migration skill within CoCo (its data-native AI coding agent) that automatically converts Apache Spark pipelines to run on Snowflake's engine. Users can migrate an entire codebase with a single conversational prompt; the tool scans for incompatibilities, rewrites unsupported patterns to DataFrame equivalents, and generates detailed reports of all changes.
Why it matters
Customers moving Spark workloads to Snowflake experience up to 5.1x faster performance and 42% lower costs, while eliminating manual cluster tuning, patching, and infrastructure management. The migration tool compresses work that previously took hours into minutes, and handles large codebases written in Python, Scala, or Java—reducing the expertise gap that generic AI assistants face when migrating at scale.
What to watch
The spark-migration skill is bundled with CoCo at no additional setup cost. Developers can invoke it by asking CoCo to migrate files or directories, assess compatibility before migration, or validate existing code—with the tool automatically flagging anything requiring manual review so users retain control over changes.
Snowflake has integrated a new spark-migration skill into CoCo, its data-native AI coding agent, designed to automate the migration of Apache Spark pipelines to Snowpark Connect—Snowflake's native Spark execution engine. The tool works from a single conversational prompt: a user can ask CoCo to "Migrate this file to Snowpark Connect" or point it at an entire directory of PySpark pipelines, and the skill will handle the conversion end-to-end.
Behind the single prompt, CoCo orchestrates a multi-step agentic workflow. First, it scans every source file for compatibility issues, including RDD operations, certain UDF serialization patterns, and unsupported file formats, building an inventory referenced against a knowledge base created by Snowflake's engineering team. Second, it applies targeted fixes by dispatching parallel agents to rewrite incompatible patterns as DataFrame equivalents, update imports and session creation, and flag anything requiring manual review with a detailed explanation. Third, it generates detailed reporting—issue logs, inventory reports, and migration state validation—that both people and other AI agents can consume. Finally, it performs optional end-to-end validation by running the original PySpark code and the migrated Snowpark Connect code against the same synthetic data and comparing outputs table-by-table; any fixes discovered during validation are automatically committed back to the deliverable branch.
The skill accepts input in Python, Scala, and Java (including build files and notebook files) and works at scale: the demo shown in Snowflake's announcement displays a large directory of PySpark pipelines scanned, rewritten, and reported on from a single prompt. Users can also scope the migration effort upfront by asking CoCo to "Assess the compatibility and level of effort required," validate a codebase before migration, or analyze and complete migrations started by other tools. The skill auto-activates when users mention Spark or PySpark, or can be invoked directly with the command "spark-migration."
Customers moving Spark workloads to Snowflake have achieved up to 5.1x faster performance and 42% lower costs, according to Snowflake's data from customer production use cases and proof-of-concept exercises conducted between November 2022 and May 2025. The migration tool is bundled with CoCo at no additional cost and requires no setup.
Running Apache Spark in production typically requires engineers to manage clusters, apply patches, tune performance, and maintain dependency environments—a significant operational burden. Snowflake's Snowpark Connect addresses this by letting existing Spark code run on Snowflake's engine with minimal code changes, eliminating infrastructure overhead and delivering measurable efficiency gains. The company's new migration tool, built into CoCo, closes a critical gap in automated migration: generic AI assistants can rewrite individual code snippets but lack the domain knowledge to migrate an entire codebase, understand which patterns are unsupported, and map them to Snowflake equivalents. CoCo's spark-migration skill reproduces the workflows a migration expert would follow—scanning for incompatibilities, applying targeted fixes in parallel, validating output against the original code, and generating detailed reports—all from a single prompt. This automation compresses a process that previously consumed hours of manual effort and cross-referencing into minutes, while keeping developers in control by flagging items requiring human review.
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