Snowflake’s data sharing is a feature that allows you to share data across Snowflake accounts without copying or physically moving the data. Instead of exporting files or building separate pipelines, Snowflake uses secure metadata pointers to let other accounts access selected databases, schemas, or tables in real time.
From a technical point of view, data sharing works through secure shares. As a Snowflake developer, I define a share, add the required objects to it, and then grant access to a consumer account. The consumer sees the shared data as a read-only database, which means the original data stays protected while still being fully queryable.
One major advantage is consistency. Since both the provider and consumer query the same underlying data, there’s no duplication and no sync issues. Performance is also well-managed — the data provider pays for storage, while the consumer pays for compute when they run queries.
As a Snowflake developer, I personally started using this feature during a project where multiple teams needed the same reporting data, but copying it was causing delays and mismatched numbers. Once we switched to Snowflake data sharing, those issues disappeared because everyone was looking at the same source of truth.
This feature becomes especially valuable during Snowflake migration projects. Instead of migrating everything in one risky move, teams can share production data with a new Snowflake environment, validate transformations, compare dashboards, and slowly switch workloads over. I’ve used this approach to run parallel systems until the business was confident enough to fully move over.
In short, Snowflake’s data sharing combines security, performance, and simplicity. It removes the need for complex data transfers and plays a big role in modern analytics and migration strategies.
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