Modernize your data estate on theSnowflake AI Data Cloud

After contending with high maintenance and a crash-prone system, Travelpass looked to Snowflake for a platform that was robust, yet easy to use; offered peace of mind when it came to data governance and cost controls; and could help tap into the vast power of AI.

Travelpass saw the benefits of true cooperation, enlisting Snowflake Professional Services to help seamlessly transition 30 TB of data and 134 pipelines/processes in just six weeks. After completing the migration, Travelpass discovered that Snowflake cut costs in half compared to its previous spend.

As a global company with teams across the Americas, Europe and Asia,Pfizer must ensure all its business units can quickly access and share data around the clock. Prior to Snowflake, this sharing required copies of data, ETL pipelines and an array of cumbersome, time-consuming processes. 

Snowgrid — Snowflake’s cross-region, cross-cloud technology layer — allows Pfizer business units to seamlessly and securely collaborate, regardless of the country they’re in. This heightened level of sharing helps improve business continuity for the global organization.

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. 

Chicago Trading Company faced expensive data transfers, frequent and unpredictable processing failures, and limited cost visibility while struggling to process vast amounts of market data.

By migrating to Snowflake, CTC eliminated data movement costs and gained visibility into spend, saving them 54% in costs. Engineers now focus on innovation instead of troubleshooting complex configurations, allowing CTC to reopen previously expensive data pipelines and explore AI capabilities.

Prior to Snowflake, data was spread across the data warehouse, data lake and production databases at WHOOP. Maintaining and scaling the company’s previous data warehouse, Amazon Redshift, was costly and operationally burdensome for the small data team. 

Since moving to Snowflake, departments across WHOOP now enjoy expedited access to data and insights, allowing them to quickly understand members’ needs and make more informed business decisions. The data team at WHOOP spends less time troubleshooting slow queries and infrastructure issues, which leaves more time for extending data access across the organization. 

PGE managed a legacy, on-premises data warehouse that was expensive to maintain and had performance issues. The system’s tightly-coupled architecture was inflexible and caused copies of data to proliferate across the organization, making it difficult to identify the authoritative source of data alongside increasing storage costs.

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.

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.