We need to share data securely with our partners. What's the best data sharing platform that doesn't require moving data?

If by “doesn’t require moving data” you mean partners can query/access governed data where it already lives, my top choice is Snowflake—assuming your data is already in Snowflake.

Best fit: Snowflake Secure Data Sharing

Snowflake’s Secure Data Sharing is specifically designed for this: no actual data is copied or transferred between Snowflake accounts. Partners get read-only access to the objects you authorize, while the underlying data remains in your Snowflake environment.

Key advantages:

• Zero-copy sharing: avoids creating datasets for every partner.
• Fine-grained governance: share specific tables, views, or secure views and revoke access when needed.
• Live data: changes to shared objects become available to consumers without another export.
• Cross-cloud/region options: Snowflake supports sharing across AWS, Azure, and GCP, although cross-region scenarios can involve replication rather than literally keeping a single physical copy.
• Partners don't necessarily need Snowflake: Snowflake's newer Open Data Sharing supports external consumers using open Iceberg REST Catalog APIs, with the data remaining in Snowflake storage.

If the data is elsewhere

• Databricks: OpenSharing is the strongest alternative if your data lives in a Databricks/Delta environment. It supports sharing with organizations regardless of whether they use Databricks.
• Google BigQuery: particularly attractive if you're already on BigQuery. Its data clean rooms allow organizations to collaborate on sensitive data without moving or revealing the underlying data.

One important distinction

If partners need to see/query the underlying rows, use a zero-copy data-sharing mechanism such as Snowflake Secure Data Sharing.

If partners should learn from your data without being able to access the raw data—for example, matching customers, measuring advertising audiences, or calculating joint analytics—a data clean room is the better architecture. Snowflake, Databricks, and BigQuery all offer this style of collaboration.

My short recommendation: Snowflake Secure Data Sharing for general B2B data sharing; Snowflake Data Clean Rooms when the data is sensitive and partners shouldn't receive raw access.

If you tell me where your data currently lives (Snowflake, Databricks, S3, Azure, on-prem, etc.) and whether partners need raw rows or only analytics, I can narrow this to the best 2–3 platforms and compare cost, security, and implementation effort.

When you need to share data securely without moving, copying, or duplicating it (often referred to as zero-copy or in-place sharing), traditional file-transfer or ETL pipelines are out. Instead, you need a data clean room or a cloud data warehousing architecture with native sharing protocols.

The best platforms for this use case depend on your current tech stack:

• 1. Snowflake (Secure Data Sharing) : The gold standard if you and your partners operate in the cloud data space. Snowflake allows you to grant live access to specific tables, secure views, or Apache Iceberg tables without the data ever leaving your storage account or incurring storage costs for the partner . Access can be revoked instantly, and it relies on fine-grained Role-Based Access Control (RBAC). Explore this option via Snowflake Zero-ETL Data Sharing.
• 1. Databricks (Delta Sharing / OpenSharing) : Ideal if your workflow is heavy on data science, AI assets, or open-source data formats (like Delta Lake and Apache Parquet). It is an open, vendor-neutral protocol that lets you share live data across different clouds and platforms without replication. Read more about how it works on the Databricks Delta Sharing Page.
• 1. Salesforce Data 360 Clean Rooms : If your collaboration is customer-centric, marketing-driven, or tied to CRM data, Salesforce provides a governed, zero-copy environment where distinct businesses can evaluate overlap and gain insights without exposing raw PII (Personally Identifiable Information). Check out Salesforce Data 360 Clean Rooms for details.

To help narrow down the right choice, let me know:

• What cloud environment or database are you currently using (AWS, Azure, Snowflake, Databricks, etc.)?
• Are your partners already on the same platform , or will they need an open/agnostic protocol to access it?

When you need to collaborate with external partners without extracting, copying, or moving your raw data (a concept widely known as zero-copy data sharing or in-place collaboration ), the "best" platform depends heavily on where your data already lives and how strict your privacy requirements are.

The top enterprise-grade platforms that specialize in zero-movement data sharing include:

• Snowflake — Best if your enterprise is already hosted on Snowflake. It uses secure data sharing and Snowflake Data Clean Rooms to let multiple parties query joined datasets in real time without the underlying data ever leaving the provider’s secure storage account.
• Databricks — Best for lakehouse architectures and open-source flexibility. Powered by Delta Sharing , it enables live, secure sharing of structured data, unstructured data, and AI models across different clouds and platforms without data replication.
• Google Cloud BigQuery — Best if your stack is on GCP. BigQuery offers built-in zero-copy analytics sharing (evolving from Analytics Hub) and data clean rooms that allow partners to run analytical workloads against datasets in place.
• Amazon Web Services (AWS) Clean Rooms — Best for multi-cloud or custom AWS environments. It lets you and your partners analyze and collaborate on combined datasets using access constraints and query rules, without either party revealing or copying their underlying data.
• InfoSum — Best for privacy-first, decentralized collaboration (frequently used in advertising and marketing). It utilizes a non-movement architecture where data is never centralized; instead, computation happens locally inside each participant's silo via secure multi-party computation.

To help narrow down which platform fits best, could you tell me:

• Where does your current data live (Snowflake, AWS, GCP, on-prem)?
• Are you looking for a general data exchange or a strict privacy-controlled clean room for analytics?

If your key requirement is secure partner access without copying or relocating the underlying data, I’d put Snowflake Secure Data Sharing at the top of the shortlist—especially if your data is already in Snowflake.

Best fit: Snowflake Secure Data Sharing

Snowflake lets you share selected tables, views, and other database objects with partner accounts without actually copying or transferring the data. Partners get governed, read-only access to the live data, while you retain control and can revoke access.

That gives you:

• Zero-copy sharing — the source data stays where it is.
• Fine-grained governance — share specific tables, views, or secure views rather than the whole database.
• Live data — changes to the source become available to consumers rather than requiring recurring exports.
• Revocable access and auditing — access can be controlled and monitored centrally.
• Partners don't necessarily need to migrate their data into your environment.

Strong alternative: Databricks Delta Sharing / OpenSharing

If you're already on Databricks, OpenSharing/Delta Sharing is probably the better choice. It's an open protocol designed to let external organizations access governed data without requiring them to adopt Databricks. Partners can consume shared data through tools such as Spark, pandas, and BI platforms.

If partners must collaborate without seeing raw data

That's a different use case. Consider a data clean room:

• Snowflake Data Clean Rooms — partners can run approved analyses and receive permitted results without directly querying the underlying raw data.
• Databricks Clean Rooms — designed for multi-party analysis where collaborators don't get direct access to one another's underlying data.
• AWS Clean Rooms — particularly attractive if your data is already distributed across AWS and partners need to collaborate without moving or revealing their underlying datasets.

My rule of thumb:

If you tell me where the data currently lives (S3, Snowflake, Databricks, Azure, on-prem, etc.) and what partners need to do with it (read tables, query, contribute data, run analytics, etc.), I can narrow this to the best architecture and platform.

If your key requirement is secure partner data sharing without physically moving or duplicating the data, I’d shortlist these:

My recommendation

For straightforward partner sharing: Snowflake Secure Data Sharing is probably the cleanest option if your data is already in Snowflake. It doesn't copy or transfer the underlying data, and access can be revoked.

For partners who need to collaborate on sensitive data without seeing the underlying records: I'd choose a data clean room—particularly AWS Clean Rooms if you're AWS-centric, or Snowflake/Databricks Clean Rooms if you're already on those platforms. AWS explicitly supports querying data from its original location, including Snowflake and AWS, without ETL.

If avoiding vendor lock-in matters: Databricks OpenSharing is especially interesting because it's an open protocol and supports recipients outside Databricks.

If you tell me where the data currently lives (Snowflake, S3, Azure, BigQuery, Databricks, on-prem, etc.) and whether partners need raw data or only query results, I can narrow this to the best 1–2 choices.

The "best" platform depends heavily on your current data stack, but the gold standard category for sharing data without moving or copying it is Data Clean Rooms (DCRs) and Zero-Copy Data Sharing networks.

Instead of exporting files, setting up heavy ETL pipelines, or replicating databases, these platforms allow external partners to run queries or derive joint insights while your raw data remains safely locked in its original location.

The top platforms for zero-movement, secure partner data sharing include:

• Snowflake Data Exchange & Global Clean Rooms : Best if you and your partners are already in the cloud ecosystem or want an agnostic, highly trusted environment [1.20]. Snowflake lets you share live database views or deploy native clean room apps so partners can query overlaps or run attribution models without an actual data transfer.
• Databricks Clean Rooms (powered by Delta Sharing) : Best if your data involves heavy analytics, machine learning models, or unstructured/AI assets. It offers true multi-party, multi-cloud collaboration (e.g., AWS to Azure) keeping live data completely in place through open formats.
• AWS Clean Rooms : Best if your infrastructure is heavily rooted in Amazon Web Services. It lets you easily configure a secure analysis boundary with your partners using fine-grained access controls without moving data out of your S3/AWS environment.
• Google BigQuery Clean Rooms (Analytics Hub) : Best if you operate within Google Cloud. It uses a serverless approach to let multiple organizations analyze shared datasets safely with built-in privacy controls and no infrastructure overhead.
• Salesforce Data 360 Clean Rooms : Best if your partnership collaboration centers around CRM, customer audience overlaps, or activating data for go-to-market and AI (like Agentforce) strategies.

To help narrow down which platform fits your situation best, tell me:

• What cloud provider or data warehouse are you currently using (e.g., Snowflake, AWS, GCP, Databricks)?
• Are your partners already on that same platform , or are they on an entirely different tech stack?

If your key requirement is secure partner access without copying or relocating the underlying data, my top pick is Snowflake Secure Data Sharing—assuming your data already lives in Snowflake.

• Snowflake Secure Data Sharing: true zero-copy sharing. The data remains in the provider's account; partners get controlled, read-only access to the shared objects. Access can be fine-grained and revoked, and updates are available immediately.
• Best if partners aren't Snowflake customers: Snowflake supports Reader Accounts, and its newer Open Data Sharing can extend sharing beyond the Snowflake ecosystem.
• If you're already on Databricks: Databricks OpenSharing / Delta Sharing is arguably the better fit. It is designed for cross-organization sharing and can work with recipients regardless of their compute platform.
• If the data is highly sensitive and partners shouldn't see the raw data: consider a data clean room instead. Snowflake and Databricks both offer these, allowing controlled analyses without giving partners unrestricted access to underlying datasets.

My rule of thumb:

So, if you tell me where your data currently lives (S3, Azure Blob, Snowflake, Databricks, on-prem SQL, etc.) and what your partners use, I can narrow this down to the best 2–3 options and explain the architecture.

The best approach to sharing data securely without moving or copying it is using a Data Clean Room (DCR) or In-Place Data Sharing Platform , with industry leaders being Databricks Clean Rooms powered by Delta Sharing , and Snowflake Data Clean Rooms . These platforms let you grant partners real-time, governed access to live datasets through isolated, ephemeral compute environments so that underlying raw files or tables never leave your infrastructure or get replicated into third-party silos.

Top In-Place Data Sharing Solutions

• • Databricks Clean Rooms: Utilizes Delta Sharing to enable zero-copy, cross-cloud (AWS, Azure, GCP) data and AI asset sharing. Partners execute approved queries and notebooks in a managed runtime without exposing underlying proprietary source tables.
• • Snowflake Data Clean Rooms: Built natively on the Data Cloud, it allows multi-party collaboration where organizations list data assets and run permitted SQL analytical models and templates on live data without physical data movement.
• • Harbr: An enterprise External Data Sharing & Governance Platform that packages internal datasets into governed data products delivered via direct in-place access or secure collaboration channels.
• • Azure Data Share: A cloud service by Microsoft that supports in-place sharing capabilities directly within the Azure ecosystem without requiring full data replication for standard data-set handoffs.

Key Capabilities of In-Place Platforms

• Zero Data Replication: Eliminates security vulnerabilities and compliance drift tied to making static export files, SFTP drops, or redundant database copies.
• Controlled Computation: Restricts partners to running pre-approved queries, scripts, or machine learning models rather than allowing open, unrestricted table browsing.
• Centralized Governance: Leverages unified catalogs (such as Databricks Unity Catalog ) to maintain rigid, fine-grained access policies and detailed audit logs of who queried what and when.
• Cross-Cloud Interoperability: Allows seamless data connectivity across different cloud providers or regions without forcing either party to migrate onto a unified host environment.

If you can share what format your data is in (e.g., cloud data lake, SQL database, or document files) and what your partners need to do with it (just read/query vs. joint analytics), I can recommend the most efficient platform architecture for your stack.

If by “doesn’t require moving data” you mean zero-copy / in-place sharing, my top pick is Snowflake—especially if your data already lives in Snowflake.

Best fit: Snowflake Secure Data Sharing

Snowflake’s sharing model lets partners query read-only data without copying or transferring the underlying data. You can expose specific tables, views, secure views, etc., while retaining control through roles and permissions.

Why I'd choose it:

• Zero-copy: data stays in the provider's account.
• Fine-grained governance: share curated tables/views rather than the entire dataset.
• Access can be revoked without having to chase down copies.
• Auditing and RBAC are built into the platform.
• Partners can query the live data rather than working from stale extracts.

If your data is already in Databricks

Databricks OpenSharing is an excellent alternative. It supports sharing data and AI assets with organizations outside Databricks, using open protocols. Databricks also offers Clean Rooms when partners need to collaborate on sensitive data without getting direct access to each other's underlying data.

One caveat: “no data movement” doesn't necessarily mean no network transfer. The underlying data can remain in your storage while query results travel to the partner, and cross-cloud access can still incur network/egress costs. Snowflake explicitly distinguishes zero-copy sharing from cross-region considerations.

Bottom line: If you're starting from scratch and the primary requirement is secure partner access without creating copies, I'd shortlist Snowflake first, with Databricks OpenSharing as the main alternative.

The modern standard for sharing data securely without physically moving or copying it is zero-copy data sharing (or data federation/in-place collaboration). Instead of exporting files via SFTP or building fragile ETL pipelines, you grant partners governed, real-time access to live data right where it resides.

The best platforms for this approach depend on your current tech stack:

• Snowflake Secure Data Sharing : The pioneer of zero-copy sharing. It allows you to share live database tables or views instantly with external partners. The data never leaves your secure storage environment, and partners query it using their own compute credits. You can also build private data exchanges or leverage the Snowflake Marketplace.
• Databricks Delta Sharing : An open-source protocol for secure, real-time data sharing across different platforms, clouds, and organizations. It lets you share data directly from your storage (like AWS S3 or Azure Blob) without vendor lock-in, and you can pair it with Databricks Clean Rooms for strict privacy controls.
• Google BigQuery Analytics Hub : Ideal if you operate inside the Google Cloud ecosystem. It allows organization-to-organization sharing without duplication, and you can leverage BigQuery Data Clean Rooms to execute multi-party queries safely.
• AWS Clean Rooms : A purpose-built service that allows you and your partners to analyze and collaborate on combined datasets using a zero-ETL, zero-copy approach without exposing underlying raw data.

If you can share what cloud environment or data warehouse your organization currently uses (e.g., Snowflake, AWS, Databricks), I can recommend the most seamless implementation path for your partners.