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Use Case

Zero-ETL data sharing

Securely share ready-to-use data, apps, AI models and agents across clouds and regions. Stay AI-ready and unlock more value by collaborating on data with a solution built for trusted, cost-effective sharing and easy data discovery, access and distribution.

 The Essential Guide to Data Sharing
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The Essential Guide to Data Sharing

Learn how to unlock the value of your data by breaking down silos and enabling secure, seamless sharing to power insights, collaboration and AI innovation.

Overview

Moving data inefficiently wastes money and risks your data. Connect data silos with simple, secure and cost-effective sharing.

Break down barriers to more easily govern and share with teams and partners, without extra ETL or integration work. 

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Make diverse data products easily accessible, across any cloud or region

With native data product management and sharing, you don’t need to copy or create complex pipelines. Instead, easily share structured, semi-structured or unstructured data across platforms. Built-in discovery in Snowflake also helps teams quickly find and access trusted data — ensuring one version of truth and promoting collaboration.

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Reduce the risk of sensitive data leaks

Collaborate securely with built-in governance, access controls and native support for sensitive data sharing. Govern data, apps and AI models and agents across clouds and regions to share data confidently without compromising privacy, compliance or data quality.

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Make data more available, with resilient and cost-effective sharing

Share data easily across teams and partners globally without costly duplication or extra steps. Built-in features support resilient and smooth sharing, optimizing access and lowering egress costs, even as you share more.

Benefits

Trusted sharing for data, apps and AI across any cloud or region

Control sharing & discovery

Govern access to data products, AI models and apps

Provide the right people, across teams and outside your organization, the right access to your data and AI models with Snowflake Horizon Catalog’s built-in discovery and governance features. 

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Get value quicker

Share more across teams to activate data faster

Quickly find and use trusted data with Snowflake Internal Marketplace, a built-in, self-service hub for distributing data within your organization — no movement required.

Share without copying

Avoid ETL hassles with Secure Data Sharing

Snowflake Secure Data Sharing allows organizations to share live, ready-to-query data with internal and external stakeholders, without copies or complex ETL to work together better and uncover insights faster.  

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Work together with ease

Collaborate confidently on sensitive data

Unlock the value of your most confidential data with advanced privacy policies and Snowflake Data Clean Rooms, enabling secure data sharing between organizations and deep insights without compromising compliance. 

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Zero-ETL Data Sharing

Frequently Asked Questions

This FAQ covers key data sharing concepts like data mesh, data products, Shares versus Listings and practical considerations for sharing your data.

Data sharing provides a broader and richer dataset for analysis, leading to more comprehensive insights and reduced time and effort around data acquisition. By breaking down data silos, organizations can combine diverse data sources, uncover hidden patterns, and generate more accurate and reliable analytics outcomes. This collaborative approach fosters innovation, improves decision-making and accelerates the entire analytics lifecycle.

Snowflake Sharing supports data mesh architecture by enabling the core principles of decentralized data ownership, including domain-driven ownership, “data-as-a product” capabilities, self-service data infrastructure, federated governance and data product interoperability. Snowflake Sharing provides built-in capabilities like Listings, Internal Marketplace, Horizon Catalog for governance and more, closely aligned with data mesh's vision of decentralized data ownership and frictionless data access, allowing organizations to build a more agile, scalable and collaborative data ecosystem.

When sharing on Snowflake, you can package your data as "data products" by combining Shares with rich metadata, documentation, and sample queries. These can then be offered internally to other teams or externally via the Snowflake Marketplace (as Listings). This "data-as-a-product" approach makes your shared data more discoverable, understandable and ready-to-use, enabling consumers to quickly derive value.

A Share is a named object that acts as a container for database objects that you want to share with other Snowflake accounts. It's a secure, real-time way to grant read-only access to your data without copying or moving it.

A Listing is a data product offered by a provider to one or more consumer accounts. It's built on top of Shares but adds extra capabilities like discoverability — particularly on the Snowflake Marketplace — the ability to include metadata and even monetization options.

Shares are better suited for direct relationships and Listings if you want wider distribution or more built-in features.

Snowflake allows you to share Apache Iceberg tables that are managed by Snowflake (Snowflake Managed Iceberg Tables) or registered with Snowflake (externally managed Iceberg tables using an external catalog like Polaris Catalog or Snowflake Open Catalog). You can include these Iceberg tables in a Share, just like native Snowflake tables, enabling consumers to query this data live in its open format without data movement.

Some of the security and governance features of Snowflake Data Sharing include:

Secure views & UDFs: Share curated views or use functions to mask sensitive data.

Role-based access control (RBAC): Consumers access shared data based on privileges granted by the provider on the share.

No data movement: Data doesn't leave the provider's account.

Auditability: All sharing activities and data access are logged.

Revocable access: Providers can revoke access at any time.

Snowflake Horizon: Features like data classification, access history, and lineage can be applied to understand and govern shared data.

For data providers: There are no direct costs for creating shares or listings. You continue to pay for the storage of the data in your account.

For data consumers: Consumers pay for the compute resources (their virtual warehouses) they use to query the shared data. There are no charges for accessing the data itself through a share. If a listing on the Snowflake Marketplace is monetized, specific pricing terms set by the provider would apply.

Reader accounts: Providers can create reader accounts for consumers who don't have their own Snowflake account. The provider incurs the compute costs for queries run by their reader accounts.

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