Snowflake optimization best practices guide

Snowflake’s AI Data Cloud is a global network that connects customers to the data, applications and models that are most critical for their businesses. Organizations successfully use Snowflake as a data warehouse to run complex analytics on large volumes of data concurrently without sacrificing performance. Due to its unique architecture, which separates storage from compute, Snowflake allows organizations to scale their data operations while paying for only what they use.

As organizations grow, their data workloads must also scale. With increasing workloads, many organizations encounter challenges identifying inefficiencies which can lead to escalating and unexpected costs. Understanding Snowflake’s architecture, the pricing structure, and how it can be optimized is vital to maximizing the use of Snowflake for a well-managed data program.

Capital One is the first U.S. bank to exit on-premise data centers and go all in on the cloud. As we worked to modernize our data operations in the cloud, we adopted Snowflake to quickly scale our data and seamlessly collaborate across our organization. However, we found we needed additional ways to control our Snowflake costs and streamline work processes at scale. We developed internally what would later become Capital One Slingshot, a cloud-based data management solution that optimizes Snowflake usage and helps businesses get the most value out of their Snowflake investment. While Snowflake provides the scalable and flexible foundation for storing and analyzing data, Slingshot’s focus is on optimizing Snowflake usage, increasing visibility into costs and streamlining workflows.