Introducing Polaris Catalog

An open source catalog for Apache Iceberg

Migrate to the Snowflake AI Data Cloud

Accelerate your migration to Snowflake.

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Migrations Master Class

Take this free course for enterprise data architects and data analytics leaders on best practices to migrate to Snowflake.
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System Integrator Partners

Accelerate Teradata migrations to Snowflake with one of 10 vetted system integrator partners in Snowflake’s Migration Accelerated initiative.

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Snowflake Professional Services

Work with our services teams to optimize, accelerate, and achieve your business goals with Snowflake.

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Native Code Conversion Tooling

Snowflake also offers best-in-class code conversion tooling to accelerate these migrations.

Migrate to Snowflake with Confidence

Most legacy systems only function as a product—Snowflake is a platform.

Single Data Platform Supporting Many Workloads and Languages:

  • Enable a full spectrum of use cases (BI and Reporting, Geospatial Analytics, etc.) across organizations — all against the same copy of data.

  • Work with SQL, Python, Java, and Scala on a single platform.

  • Power a full spectrum of data formats (structured, semi-structured, and unstructured) and architecture patterns (data warehouse, data lake, a hybrid of the two, data mesh, and more).

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It Just Works

  • Snowflake’s fully managed service automates as much as possible so that your teams can focus on what matters to the business.
  • Get automatic provisioning, high availability, performance optimization, data protection, metadata management, and more.
  • Available across clouds and regions—for an near-unlimited number of users and jobs.
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Migrate to Snowflake from Your Legacy System


Legacy Hadoop



Cloud Big Data



Big Query


With a relatively limited data stack built around AWS Redshift, Lucid had no ability to personalize customer engagements across channels.


Searching for a robust data platform that could act as both a data lake and a data warehouse, Lucid turned to Snowflake. Snowflake consolidates Lucid’s platform into a single centralized platform and lets the company ingest data from multiple sources.

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Marriott was an early adopter of Netezza and Hadoop, leveraging the IBM BigInsights platform. Many of those technologies made the stack complex, costly due to expensive upgrades, and difficult to operate.


Simplifying its data platform on Snowflake enabled Marriott to achieve transparency and control of its data, faster speed to market, improved collaboration and data sharing, and lower TCO. 

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AT&T had a highly tuned, interactive reporting application built on Teradata. With over 100k internal users initiating over 2 million data calls per day, it leveraged every Teradata tool to achieve the sub-second data response time. 


The migration to Snowflake gave AT&T the ability to leverage result caching in addition to warehouses. The result was a cost-effective alternative with equivalent performance.

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PGE managed a legacy, on-premises data warehouse that was expensive to maintain and had performance issues. 


Realizing the need for a modern data environment, PGE turned to Snowflake. It chose the Snowflake Data Cloud on AWS for its high performance, separation of storage from compute, near-zero maintenance, and micro-partitioning.

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Autodesk’s data lake architecture was operationally burdensome to support and cost-prohibitive to scale. Data ingestion workloads relied on large amounts of homegrown code that led to frequent troubleshooting and unreliable data. 


Migrating data, users and workloads to Snowflake helps Autodesk achieve a highly efficient and cost-effective data environment. ETL processes now run in minutes, not hours.

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Sanofi’s data team faced several challenges with Spark’s manual deployment and maintenance, resource scalability issues, concurrency problems during peak usage, and data movement complexities between multiple platforms.


Snowflake’s separation of storage and compute, near-zero maintenance and on-demand scalability allowed Sanofi to efficiently handle increased workloads and data volumes without compromising performance.

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For several years, Siemens has been rethinking how it uses data, looking to gain key operational insights that power decision-making and automate processes to unlock innovation.


With Snowflake, Siemens created the Siemens Data Cloud, an open data mesh platform ecosystem that enables cloud transformation, centralizes data, improves decision-making and scales the use of AI.

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Before Snowflake, Insider faced unpredictable costs on Big Query and had difficulty scaling their solutions. 


By migrating to Snowflake, Insider now has fine-grained control of their compute and storage costs, resulting in a reduction of their month-over-month spend by 23%.

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of Teradata, Oracle, and Big Data (Hadoop, Spark, etc.) migrations to Snowflake


of SQL Server, Cloud Provider migrations to Snowflake

< 4 Months

to migrate 5000 tables + 5TB of Data for Micron when migrating from a legacy data warehouse to Snowflake

< 1 Day

to set up data sharing capabilities in Snowflake, vs. 6-8 weeks it previously took Tapestry when using Hadoop



Near-instant elasticity right-sizes any number of users, jobs, or data

Ongoing Improvements

Ongoing performance improvements and cost optimization means your price for performance only gets better.

Consumption-Based Pricing

Per-second consumption-based pricing ensures you’re only charged for what you use.    

Calculate Your Migration ROI

This modeling tool is designed to help you generate a financial business case for migrating to Snowflake on AWS.

Migration Accelerated


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