How does Snowflake work? A simple explanation of the popular data warehouse

Snowflake is a cloud data warehouse that can store and analyze all your data records in one place. It can automatically scale up/down its compute resources to load, integrate, and analyze data.

As a result, you can run virtually any number of workloads across many users at the same time without worrying about resource contention. Workloads can include use cases such as batch data processing to interactive analytics to complex data pipelines.

Consider a typical scenario where teams want to run different queries on customer data to answer various questions. Your product team may want to understand engagement and retention, while your marketing team may want to understand acquisition costs and customer lifetime value. Running all these queries on one compute resource cluster would create competition for resources, slowing query performance for both teams. But with Snowflake, you can create separate virtual warehouses for each team, allowing all stakeholders to quickly get the answers they need.

Snowflake also automatically creates another compute cluster instance whenever one cluster is unable to handle all incoming queries—and starts balancing loads between the two clusters—so you never need to worry about downtime or slow performance.

Because Snowflake can scale on-demand capacity and performance as needed, data teams no longer need to run upfront capacity planning exercises. Nor do they need to maintain costly oversized data warehouses that remain mostly underutilized.