What is zero-copy data sharing?

Zero-copy data sharing is the mechanism by which one organization gives another direct access to its own data without physically copying it into a second system. The sharing party exposes metadata references that point to the original storage; the receiving party queries that data as if it lived in its own environment, but is always reading the same source copy, updated in real time. This is a different mechanism from a data clean room, which exists to let competing organizations cross-match data without either seeing the other's raw records: there is no neutral computation over obscured data here, just direct, transparent access to a partner's, supplier's or customer's data, governed by permissions the owner defines and can revoke at any time. The distinction matters because it drives the choice of tool: if the goal is collaborating without exposing each other's data, you need a clean room; if the goal is giving a trusted partner direct visibility into your own data, you need zero-copy sharing.

See also: Data branching · An isolated working copy of data created without duplicating it, with Git's commit, promotion and rollback semantics.

How the major vendors implement it

Every major data platform has its own implementation, and the differences matter when choosing a stack. Snowflake Secure Data Sharing creates "shares" that expose database objects to other Snowflake accounts: within one region the share is direct and zero-copy, without ever moving the data; across regions or cloud providers it goes through replication, with a Snowflake-managed copy in each region and no ETL on the consumer. It works well between two Snowflake tenants, but stays tied to that ecosystem. Databricks' Delta Sharing is an open protocol, not proprietary to a single vendor: recipients can read the shared data with any client that speaks the protocol (Spark, pandas, Power BI), even without running Databricks themselves. BigQuery Analytics Hub follows a similar logic to Snowflake's but inside the Google Cloud ecosystem, with an internal catalog for publishing and discovering shared datasets across projects and organizations. The advantage common to all three approaches is avoiding the ETL pipelines many companies still build just to synchronize duplicate copies of someone else's data: no more overnight jobs exporting, transforming and reloading, no drift between the copy and the source.

A concrete enterprise example

A supplier managing inventory for a network of enterprise retailers can share stock levels in real time without building a daily export to each customer. With zero-copy sharing, each retailer queries the supplier's live data directly, with the guarantee that the numbers they see always match the source system, not last night's snapshot.

Why it matters for decision makers

The main risk is lock-in: if the sharing format is proprietary to a single vendor, moving to another data platform means rebuilding the entire integration with every connected partner, not just migrating internal data. The second issue is access governance: the sharing party must be able to define precisely who sees what, for how long and under what re-export restrictions, because the technical simplicity of zero-copy sharing does not replace a clear data governance agreement between the parties.