Unlocking the Power of Snowflake's Data Clean Room

Unlocking the Power of Snowflake's Data Clean Room

Table of Contents

1. Introduction
2. What is Snowflake's Data Cleanroom Architecture?
3. Components of Snowflake's Data Cleanroom Architecture
   • Row Access Policies
   • Store Procedures
   • Data Shares
   • Streams and Tasks
4. Design Pattern for a Basic Two-Party Clean Room
5. Leveraging Existing Snowflake Functionality
6. Query Templates and Available Values
7. Generating and Validating Query Requests
8. Access Control through Row Access Policies
9. Data Enrichment at the Intersection of Datasets
10. Managing Approved Query Requests
11. Demo of Snowflake's Data Cleanroom in Action
12. Conclusion

Snowflake's Data Cleanroom Architecture: Collaborating with Privacy

Snowflake's data cleanroom architecture provides a solution for organizations to collaborate and share data while maintaining high levels of data privacy. In this article, we will explore Snowflake's data cleanroom architecture, its components, and how it enables collaboration across organizations. We will also discuss the design pattern for a basic two-party clean room and the process of generating and validating query requests. Additionally, we will examine the access control measures and data enrichment possibilities offered by Snowflake's data cleanroom. Finally, we will conclude with a demo of the clean room in action.

Introduction

In the era of data-driven decision-making, organizations are increasingly collaborating and sharing data to gain insights and drive innovation. However, data privacy and security concerns pose significant challenges when it comes to data sharing. Snowflake's data cleanroom architecture offers a solution by allowing organizations to collaborate while maintaining strict data privacy controls.

What is Snowflake's Data Cleanroom Architecture?

Snowflake's data cleanroom architecture is a design pattern that leverages existing functionality within the Snowflake database. It enables organizations to collaborate and share data while ensuring the highest levels of privacy and security. Unlike traditional data sharing approaches that require copying or moving data between systems, Snowflake's data cleanroom architecture allows organizations to maintain full control over their data and share it securely with trusted partners.

Components of Snowflake's Data Cleanroom Architecture

Snowflake's data cleanroom architecture is composed of several key components that work together to enable secure data sharing and collaboration. These components include row access policies, store procedures, data shares, and streams and tasks.

Row Access Policies

Row access policies act as a data firewall, controlling access to specific rows of data Based on predefined rules. They allow organizations to define granular access controls and ensure that only authorized queries can access sensitive data.

Store Procedures

Store procedures are used to automate data processing tasks and enforce data privacy controls. They generate and validate query requests, ensuring that queries are within predefined boundaries and match approved templates and available values.

Data Shares

Data shares facilitate the one-way, Read-only sharing of data between parties. They allow organizations to securely share data without physically moving or copying it. Data shares provide a controlled way for parties to access and join datasets while maintaining strict privacy controls.

Streams and Tasks

Streams and tasks automate workflows within the data cleanroom environment. Streams capture and deliver data changes in real-time, while tasks use these changes to trigger actions such as generating and validating query requests.

Design Pattern for a Basic Two-Party Clean Room

The basic two-party clean room design pattern involves a data provider or publisher (Party One) and a data consumer (Party Two). Both parties have tables in their respective Snowflake accounts that contain data related to their customers. The clean room environment allows them to unlock more value by leveraging the intersection of their datasets.

To establish the clean room environment, Party One and Party Two need to agree on the types of queries the consumer can ask about the provider's data. These queries take the form of flexible query templates that act as guard rails to keep the queries within predefined boundaries. Query templates include substitution parameters that allow the consumer to choose from a list of available values. These available values are stored in the Party One available values table.

When an analyst from Party Two wants to run a query against Party One's data, they select a query template and one or more available values. These selections are merged and stored as a Record in the query request table on Party Two's database. The validate query stored procedure reads the query request records through a Snowflake stream and compares them with the query template and available values tables. If the request is valid, it is marked as approved, and the fully formed query is stored in the approved query requests table.

Access to the tables in each party's database is enabled through the use of Snowflake data shares. Party Two shares their query request table with Party One, while Party One shares their query templates, available values, and request status tables with Party Two. Additionally, Party One shares their customers table with Party Two, but access to this table is highly restricted through a row access policy.

Once an approved query is executed, the result set can be saved to local tables in Party Two's database. This data enrichment process ensures that only the necessary and approved data is shared, while maintaining high levels of privacy and security.

Leveraging Existing Snowflake Functionality

One of the key advantages of Snowflake's data cleanroom architecture is that it leverages existing functionality within the Snowflake database. This means that organizations with a Snowflake account already have everything they need to deploy a data cleanroom. The architecture is not a separate product offering or platform but a design pattern that utilizes existing Snowflake functionality.

By leveraging Snowflake's built-in row access policies, store procedures, data shares, and streams and tasks, organizations can establish a secure and privacy-preserving environment for data collaboration.

Query Templates and Available Values

Query templates are at the Core of Snowflake's data cleanroom architecture. They define the types of queries that can be asked about a party's data and act as guard rails to prevent queries that go beyond predefined boundaries. Query templates include substitution parameters that allow the consumer to select from a list of available values.

Available values provide the consumer with options to choose from when running a query. They allow for flexible querying while ensuring that the queries stay within the predefined boundaries defined by the query templates. Available values are stored in the Party One available values table and can include multiple options for each substitution parameter.

The combination of query templates and available values enables collaboration between parties while maintaining control over the types of queries that can be executed.

Generating and Validating Query Requests

The process of generating and validating query requests is crucial in Snowflake's data cleanroom architecture. It ensures that only approved, valid queries are executed and that the data shared between parties complies with predefined rules and boundaries.

When a consumer wants to run a query against a provider's data, they select a query template and one or more available values. These selections are merged and stored as a record in the query request table. The generate query requests stored procedure handles this process, ensuring that the selections are within the predefined boundaries.

On the provider's side, the validate query stored procedure reads the query request records by using a Snowflake stream. It compares the requests with the query templates and available values to validate their authenticity. If a query request is valid, it is marked as approved, and the fully formed query is stored in the approved query requests table. If a request is invalid, it is declined and not executed.

This process ensures that queries are thoroughly validated and comply with the predefined rules and boundaries set by both parties.

Access Control through Row Access Policies

Row access policies play a crucial role in Snowflake's data cleanroom architecture by providing granular access control to specific rows of data. They act as a data firewall, only allowing access to sensitive data through approved queries stored in the query request table.

Row access policies ensure that unauthorized queries or attempts to access the provider's data are denied. They define the conditions under which data can be accessed, such as matching specific attributes or a combination of attributes common to both datasets. This access control measure adds an additional layer of security and privacy to the cleanroom environment.

Data Enrichment at the Intersection of Datasets

Snowflake's data cleanroom architecture allows organizations to unlock more value by enriching datasets at their intersection. By leveraging the cleanroom environment, parties can pull and merge Relevant data from each other's datasets without compromising data privacy.

In the example Scenario, Party Two is able to request data from Party One's customers table and enrich their own dataset with demographic information. By joining datasets based on common attributes, Party Two gains access to valuable insights while maintaining strict privacy controls. This data enrichment process is highly customizable and can include additional attributes and data sources as needed.

Managing Approved Query Requests

Approved query requests are the gateway to accessing data in Snowflake's data cleanroom environment. These requests are fully formed queries that have been validated and approved by the provider.

The approved query requests table contains the history of validated queries, allowing both parties to monitor and track the queries executed within the cleanroom environment. This table serves as a central reference for accessing approved queries and their results.

Management of approved query requests ensures that only authorized queries are executed and that the shared data complies with predefined boundaries and rules.

Demo of Snowflake's Data Cleanroom in Action

To better understand Snowflake's data cleanroom architecture, let's walk through a demo showcasing the various components and processes involved.

1. Data Provider:

   • Share query templates, available values, and request status tables with the data consumer.
   • Share the customers' table with the data consumer using a highly restricted data share controlled by a row access policy.
   • Generate and validate query requests using store procedures.

2. Data Consumer:

   • Select a query template and available values to define the requested query.
   • Store the query request in the data cleanroom database.
   • Validate the query request against the templates and available values provided by the data provider.
   • Execute approved queries and save the results to local tables.

This demo illustrates the collaboration and data sharing capabilities of Snowflake's data cleanroom architecture while maintaining strict privacy controls.

Conclusion

Snowflake's data cleanroom architecture provides organizations with a secure and privacy-preserving environment for collaborating and sharing data. By leveraging existing functionality within the Snowflake database, organizations can maintain control over their data while enabling valuable insights through data enrichment at the intersection of datasets.

In this article, we explored the components of Snowflake's data cleanroom architecture, the process of generating and validating query requests, and the access control measures provided by row access policies. We also discussed the benefits of leveraging Snowflake's existing functionality and demonstrated the cleanroom architecture in action.

With Snowflake's data cleanroom architecture, organizations can collaborate with confidence, knowing that their data privacy and security are preserved. By embracing this architecture, organizations can unlock the full potential of their data while maintaining the highest standards of privacy and compliance.

Highlights

• Snowflake's data cleanroom architecture enables collaboration while maintaining data privacy.
• The architecture leverages existing Snowflake functionality, making it easy to deploy.
• Query templates and available values ensure queries stay within predefined boundaries.
• Row access policies act as a data firewall, controlling access to specific rows of data.
• Data enrichment at the intersection of datasets unlocks valuable insights.
• Snowflake's data cleanroom architecture provides a secure and privacy-preserving environment for data collaboration.

FAQs

Q: What is Snowflake's data cleanroom architecture? A: Snowflake's data cleanroom architecture is a design pattern that enables organizations to collaborate and share data while maintaining data privacy and security. It leverages existing functionality within the Snowflake database, such as row access policies, store procedures, data shares, and streams and tasks, to provide a secure and privacy-preserving environment for data collaboration.

Q: How does Snowflake's data cleanroom architecture ensure data privacy? A: Snowflake's data cleanroom architecture ensures data privacy through several mechanisms. Row access policies act as a data firewall, controlling access to specific rows of data. Query templates and available values restrict the types of queries that can be executed. Approved query requests are the only gateways to access data, and data shares provide one-way, read-only access to data. Combined, these measures ensure that data privacy is maintained throughout the collaboration process.

Q: Can Snowflake's data cleanroom architecture be extended to multiple parties? A: Yes, Snowflake's data cleanroom architecture can be extended to support multiple parties. The basic two-party cleanroom design pattern can be expanded to include additional parties, enabling a more complex network of data collaboration. Parties can share query templates, available values, and data with each other, as well as create custom row access policies to control data access.

Q: Does Snowflake's data cleanroom architecture support real-time data sharing? A: Yes, Snowflake's data cleanroom architecture supports real-time data sharing through the use of streams and tasks. Streams capture and deliver data changes in real-time, allowing parties to keep their datasets up to date. Tasks can then be triggered based on these data changes, enabling the automatic generation and validation of query requests. This real-time capability enhances the responsiveness and agility of the data cleanroom environment.