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

If your requirement is “share insights with partners without moving or copying the underlying data,” you’re generally looking for a data clean room or secure data collaboration platform, not a traditional file-sharing tool. These platforms let partners run approved analyses while keeping each party’s raw data under their own control.

The best choice depends on where your data already lives:

A practical recommendation

• If your data is already in Snowflake: start with Snowflake Data Clean Rooms.
• If your data is in AWS: AWS Clean Rooms is usually the most natural fit.
• If you run Databricks: use Databricks Clean Rooms.
• If partners are non-technical or the use case is marketing/customer matching: consider Salesforce Data 360 Clean Rooms or InfoSum.
• If you need broad enterprise governance: prioritize platforms with audit logs, granular permissions, query restrictions, and privacy-enhancing technologies.

Key capabilities to require in any evaluation:

• No raw data export or replication
• Fine-grained access controls
• Query/output restrictions (for example, preventing small-group identification)
• Audit trails
• Encryption and privacy-enhancing computation
• Support for your existing warehouse/lake

If you tell me your current stack (Snowflake, AWS, Azure, Databricks, Google Cloud, on-prem, etc.) and what partners need to do (analytics, ML, reporting, customer matching, data products), I can narrow this to a short list.

When you need to collaborate with external partners without the security risks, costs, and lag of ETL pipelines or file drops (SFTP), you are looking for a Zero-Copy Data Sharing architecture or a Data Clean Room (DCR).

Instead of moving the data to the partner, these platforms allow your partners to query or analyze data securely while it remains stationary in your system of record.

The leading platforms categorized by your existing tech stack and use case include:

• Snowflake (Data Sharing & Clean Rooms) : The gold standard for direct database-to-database zero-copy sharing. If you and your partners use Snowflake, you can grant granular, read-only access to live database tables or views instantly. Data updates are real-time, and you maintain complete revocation control without any files changing hands. They also offer native Data Clean Rooms for privacy-safe multi-party analytics.
• Databricks (Delta Sharing) : An open-source protocol for secure zero-copy data sharing across different clouds, platforms, and organizations. Delta Sharing lets you share data directly from your data lakehouse to a partner's environment (whether they use Databricks, Pandas, PowerBI, or Spark) without copying the underlying files or requiring both parties to be on the same platform vendor.
• Salesforce Data Cloud & Data 360 Clean Rooms : Excellent if your collaboration is centered around customer data, CRM, and go-to-market/AI strategies. Salesforce uses a "Bring Your Own Link" (BYOL) zero-copy federation approach that connects directly with platforms like Snowflake, BigQuery, and Redshift, allowing secure joint analysis without moving raw data records.
• Google BigQuery (Analytics Hub) : Ideal if your infrastructure lives in Google Cloud. Analytics Hub allows organizations to create secure data exchanges, publishing live datasets and virtual views to external partners with enterprise-grade IAM governance and zero physical duplication.

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

• What cloud provider or data warehouse (Snowflake, AWS/S3, GCP/BigQuery, Azure) do you currently use?
• Is the goal to let partners run queries on your data, or do you need a neutral privacy-safe environment (Data Clean Room) to blend both sides' data together?

If your key requirement is secure partner access without copying/moving the underlying data, I’d shortlist Snowflake Secure Data Sharing and Databricks Delta Sharing/OpenSharing.

My default pick: Snowflake

Snowflake is particularly strong if your data already lives in Snowflake. Its Secure Data Sharing provides read-only access without actually copying or transferring the data; the provider controls which objects are shared and can revoke access.

When Databricks is better

Databricks is compelling if your data is in a lakehouse/Delta environment or your partners use heterogeneous tools. Its OpenSharing/Delta Sharing approach lets external organizations access governed data without requiring them to run Databricks, and Databricks describes the sharing model as avoiding data copies.

If partners must collaborate on sensitive data

Use a data clean room rather than ordinary sharing. Snowflake and Databricks both provide clean-room capabilities where partners can run controlled analyses without getting direct access to one another's raw data.

Rule of thumb:

• Data already in Snowflake → Snowflake Secure Data Sharing
• Data in Databricks/Delta Lake → Databricks OpenSharing/Delta Sharing
• Multiple parties need to analyze sensitive data together → Data Clean Room
• Need simple file exchange → a secure file-transfer platform may be cheaper, but it inherently involves moving/copying data

If you tell me where your data currently lives (Snowflake, S3, Azure, BigQuery, Databricks, on-prem, etc.) and whether partners need raw data or just query/analysis access, I can recommend the best architecture.

When Databricks is better

Databricks is compelling if your data is in a lakehouse/Delta environment or your partners use heterogeneous tools. Its OpenSharing/Delta Sharing approach lets external organizations access governed data without requiring them to run Databricks, and Databricks describes the sharing model as avoiding data copies.

If partners must collaborate on sensitive data

Use a data clean room rather than ordinary sharing. Snowflake and Databricks both provide clean-room capabilities where partners can run controlled analyses without getting direct access to one another's raw data.

Rule of thumb:

A data clean room or a zero-copy data sharing platform is the best choice when you need to share data securely without moving or copying the underlying files. These systems let partners query live data in place while applying strict privacy controls.

Top Zero-Copy Platforms

• Snowflake Data Sharing : Allows direct sharing of live tables and secure views across different accounts and clouds without copying physical files. Recipients query the data using their own compute resources.
• Databricks Delta Sharing : An open protocol that lets you share live data tables directly from your data lakehouse to external partners using standard tools and Apache Iceberg clients without data replication.
• Salesforce Data Cloud Clean Rooms : Built on a zero-copy foundation to let brands analyze partner overlaps and campaign metrics in a secure space without exposing raw customer records.

Key Benefits

• No Data Movement : Files stay in your storage environment, eliminating storage waste and data drift.
• Live Access : Partners always see the most up-to-date information.
• Granular Control : You define exact permissions, row-level filters, or aggregate-only access that you can revoke instantly.

If you tell me what cloud or database your company currently uses (like AWS, Azure, GCP, or Snowflake), I can give you a more specific recommendation for your setup.

If by “doesn’t require moving data” you mean zero-copy sharing—partners can access governed data where it already lives rather than receiving exported files—there are two standout choices:

• snowflake.com Secure Data Sharing — my top choice if your data is already in Snowflake. It shares selected tables/views without copying or transferring the underlying data, provides read-only access, and lets you revoke access or update the shared data centrally.
• databricks.com Delta Sharing / OpenSharing — my top choice if your data is in a lakehouse or you need partners using different technologies. Delta Sharing is an open protocol designed to let external organizations access live data without replicating it, including from tools such as Pandas and Spark.

Which I'd choose

For particularly sensitive partner collaboration, both ecosystems also have clean-room capabilities, where partners can run approved analyses without getting direct access to each other's raw data.

Bottom line: If you tell me where your data currently lives (Snowflake, S3, Azure Data Lake, BigQuery, Databricks, PostgreSQL, etc.) and what your partners need to do with it (query, download, BI, join with their data, ML), I can recommend the best architecture and a few specific platforms.

The best platforms for secure, zero-copy data sharing —meaning you grant governed access to live data without moving, copying, or duplicating it—are Snowflake (using Secure Data Sharing) and Databricks (using Delta Sharing), alongside Data Clean Rooms like Salesforce Data 360 Clean Rooms for strict multi-party privacy.

Top Zero-Copy Data Sharing Platforms

• Snowflake Secure Data Sharing: Allows you to share live, curated data with external partners instantly. Partners can query the data directly through real-time views without any data transfer, ETL pipelines, or storage duplication costs on the consumer side.
• Databricks Delta Sharing: An open-source, secure protocol that enables sharing live data across different platforms, clouds, and organizations without copying underlying files. It provides governance via Unity Catalog so you retain total control over who accesses what.
• Salesforce Data 360 Clean Rooms: A neutral, zero-copy environment designed for secure enterprise collaboration and AI analytics. It allows multiple parties to match and analyze combined data insights without exposing raw underlying records.

Platform Comparison

To help narrow down the ideal platform for your team, could you share:

• Are your partners already using a specific cloud warehouse (like Snowflake or Databricks)?
• Do you need strict privacy controls (like masking and aggregation via a clean room), or direct analytical query access?

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

My recommendation

If you already have Snowflake, use Snowflake Secure Data Sharing. It is probably the cleanest fit: Snowflake explicitly states that shared data isn't copied or transferred between accounts, while access remains read-only, governed by roles, and revocable.

If your data lives in a lakehouse or cloud object storage and you want maximum interoperability, I'd favor Databricks OpenSharing/Delta Sharing. It's based on an open protocol and lets external organizations access governed data without requiring them to adopt Databricks.

If the partners shouldn't actually receive the underlying data, use a data clean room instead. Both Snowflake and Databricks support privacy-preserving collaboration where partners can run approved analyses without direct access to each other's raw data.

So, in one line:

Snowflake if you're already on Snowflake; Databricks/Delta Sharing if you need open, cross-platform sharing; Clean Rooms if the data is particularly sensitive. If you tell me where your data currently lives (Snowflake, S3, Azure, BigQuery, Databricks, SQL Server, etc.) and whether partners need raw rows or just query/analysis access, I can recommend the best architecture.

When you need to share data securely without moving, copying, or duplicating the underlying files (often referred to as zero-copy data sharing or in-place data collaboration ), the "best" platform depends heavily on where your data currently lives and how tightly integrated you want to be with your partners.

The top enterprise solutions that let you leave data in its original home while granting secure, governed access include:

1. Snowflake (Best for Ecosystem-Agnostic & SQL-based Sharing)

• How it works: Snowflake’s Secure Data Sharing feature allows a data provider to grant access to specific tables, databases, or secure views without actually transferring or copying any bytes. The consumer queries the data using their own compute resources, reading live from the provider's storage layer via metadata mapping. It also supports open table formats like Apache Iceberg.
• Pros: No data duplication (zero storage costs for the consumer side), real-time updates (if you update a record, the partner sees it instantly), granular role-based access control, and cross-cloud/cross-region capabilities.
• Cons: Both you and your partner ideally need to operate within Snowflake (though Reader Accounts can be spun up for non-Snowflake partners).

2. AWS Clean Rooms (Best for Privacy-Preserving & Multi-Party Collaboration)

• How it works: AWS Clean Rooms lets multiple parties analyze and collaborate on combined datasets together without either party needing to see, move, or copy their raw underlying records from their respective cloud environments.
• Pros: Built for strict privacy-enhancing controls (such as query restrictions, aggregation limits, and differential privacy). It supports zero-ETL collaboration across AWS and even external partners on platforms like Snowflake.
• Cons: Geared heavily toward analytical queries, joint marketing measurement, and data clean room use cases rather than general transactional database sharing.

3. Databricks Delta Sharing (Best for Open-Source & Lakehouse Architectures)

• How it works: Delta Sharing is an open, secure protocol for secure data sharing that lets you share data in data lakes and lakehouses directly from cloud storage (AWS S3, Azure Blob, Google Cloud Storage) without requiring the recipient to be on Databricks.
• Pros: Open-source foundation, platform-agnostic (clients can connect via Python, Pandas, Apache Spark, or BI tools), and eliminates heavy ETL pipelines.
• Cons: Requires technical overhead to manage the Delta Sharing server if partners are not using a managed Databricks workspace.

Which one should you pick?

• Choose Snowflake if you and your partners are already in the data cloud ecosystem or need high-performance SQL-based live sharing.
• Choose AWS Clean Rooms if your primary concern is strict privacy, restricting what partners can deduce from overlapping data, or multi-party analysis.
• Choose Databricks Delta Sharing if you are heavily invested in open data formats (like Parquet/Delta Lake) and want an open-source, non-proprietary sharing standard.

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

• Where is your data currently stored (AWS, Azure, GCP, on-prem)?
• Are your partners already using a specific cloud platform or data stack?

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. [1][2][3]

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][2]
• 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]
• 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. [1][2]

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?