Migrate data warehouse code and objects to Snowflake with automated conversion for tables, views, ETL, reporting and procedures across major platforms. Analyze Spark codebases, assess Snowpark compatibility, and transform eligible APIs into Snowpark equivalents for faster modernization.
Snowflake AIM unifies SnowConvert AI and Snowpark Migration Accelerator technologies to assess and migrate data and code to Snowflake. Analyze Spark code, convert APIs to Snowpark, and modernize warehouses, tables, views, ETL, reporting and procedures across major platforms.
Unify assessment, conversion, migration, and validation in one guided experience. Snowflake AIM uses AI to streamline every step — from source connection to deployment—reducing complexity and accelerating time to value.
"With Snowflake, we now deliver a much better data platform at global scale for less than our legacy data platform."
Snowflake AIM builds a functional model of your codebase, not just pattern matching, enabling more accurate conversion of complex SQL and Spark workloads into optimized Snowflake and Snowpark implementations.
“Switching from the previous Snowflake connector to Snowpark Connect delivered massive results: We saw the runtime drop from 1.5 hours all the way down to about 25 minutes.”
Handle migrations of any size with consistent performance. Whether modernizing a single workload or an enterprise ecosystem, Snowflake AIM scales efficiently across data warehouses, ETL pipelines, and BI environments.
“It’s important to have one platform that connects every data source.”
Ensure data accuracy and functional equivalence with integrated validation. Test, reconcile, and verify data and code throughout the process with automated checks, reusable rules, and detailed reporting.
“We’re coming up on three years of working with Snowflake, and it’s still the obvious choice. We feel completely happy with the decision we made.”
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Indeed Reimagines Architecture and Data Collaboration to Help Job Seekers and Employers without Data Movement
With Snowflake’s native support for Iceberg tables, Indeed converted its 52-petabyte data lake, breaking down data silos and allowing analysts to directly read and write Iceberg tables through Snowflake.
Answers to common Snowflake migration questions, including coverage, automation, Spark support, rewrites, validation and how to migrate faster with less risk.
Observe by Snowflake unifies logs, metrics and traces in one platform. That gives engineering teams a shared view of system behavior so they can move from signal to root cause without jumping between disconnected tools.
AI SRE helps teams investigate incidents using natural language, correlate signals across logs, metrics and traces and surface likely causes faster. Teams can troubleshoot in a single workflow instead of piecing together context across multiple tools.
Yes. Observe by Snowflake supports infrastructure monitoring across cloud services, Kubernetes, containers and serverless environments. Teams can connect infrastructure signals to the services and workloads they affect for faster troubleshooting.
Observe by Snowflake helps teams trace AI requests from prompt to response across multi-step agentic workflows. It also helps monitor latency, errors and token usage so teams can improve reliability, control spend and understand how AI application behavior connects to underlying services.
Yes. Observe by Snowflake works with OpenTelemetry and common collection methods so teams can bring in telemetry from existing environments without rebuilding their workflows. That makes it easier to centralize operations data while keeping collection flexible.
Observe by Snowflake helps teams control costs by using Snowflake’s architecture to separate storage and compute, retain more telemetry economically and reduce the overhead of fragmented tools and rigid data tiers. That helps teams scale visibility without scaling complexity at the same rate.
