Great news for developers ready to build, test, and ship Data Cloud-powered apps: We are excited to share that Data Cloud capabilities can now be developed and tested within your existing sandbox environments! This enhancement provides a unified testing ground for developers, combining the robust features of Data Cloud with the existing Salesforce data, configurations, and workflows already in place. By refreshing your existing sandboxes or spinning up new ones, developers can now use all capabilities of Data Cloud in a secure, isolated setting, enabling you to test, customize, and enable user training on Data Cloud without impacting live production environments.

Salesforce sandboxes have long been crucial for safe development and testing, allowing teams to work with configurations and data in an isolated environment that mirrors production without any risk to actual business operations. With Data Cloud now a part of these sandboxes, you can enhance innovation and reduce risks, ensuring that every aspect of your Salesforce ecosystem works harmoniously before any changes are moved to production.

Here are some examples of capabilities you can now build, test, and deploy in a sandbox: 

  • Data Streams, Models, and Calculated Insights: Experiment with integrating various data sources into Data Cloud. Test functionality for importing and modeling data; create a strategy for federated, zero copy access to other lakes and warehouses; and ensure mappings are accurate, and that data transformations function as expected. You can even create new Calculated Insights, and then surface those multidimensional metrics across your CRM to explore new and valuable ways to take action on your unified data without fear of business interruption.
  • AI and Analytics: Configure and test AI tools within a sandbox to verify that they deliver precise insights and accurate predictions. Create new models in Model Builder using training data you stream in, or import models from existing ML platforms such as Databricks, Google Vertex AI, and Amazon SageMaker, then validate them with your business analyst teams. Or create a new search index and design Search Retrievers in Einstein Prompt Builder that use semantic search to unlock insights from unstructured content. You can even experiment with new reports on Data Cloud data before delivering them to your end users – all in isolation from existing business processes. 
  • Data Cloud-Triggered Flows: Prototype, test, and deploy new automations that are triggered off of changes to Data Cloud tables. Use zero copy integration to bring in data from existing lakes or warehouses (like inventory data from Google BigQuery), and see how that can be used, for example, to fuel a backorder notification to a customer when an item is in stock. Development teams can scaffold new Data Cloud-triggered automations alongside existing flows and flow orchestrations to see how they can deliver end-to-end process automation using all of their critical business data, then validate their work with business users before deploying to production.
  • Sales Data Optimization: Connect, harmonize, and fine-tune diverse data sources to predict customer behaviors and key metrics like likelihood to close and revenue generation. In Partial and Full-copy sandboxes, your CRM data will automatically be replicated, making it immediately available for use with Data Cloud. This enables the creation and testing of automated sales workflows, like opportunity routing and forecast updates, and personalize engagements, enhancing decision-making and increasing efficiency through AI-driven insights utilizing your actual data. 
  • Enhanced Customer Service: Increase customers satisfaction and agent productivity by creating a comprehensive view of the customer. Similar to sales, in your Partial and Full-Copy sandboxes, your service data will be automatically replicated. This allows your teams to implement intelligent automation to increase case deflection, CSAT, and provide agents proactive alerts to assist their customers effectively. You can also develop and test personalized recommendations for customer interactions based on service history, transaction history, product telemetry data, and more.
  • Personalized Marketing: Create new segments – including through the use of generative AI – to explore untapped audiences your marketing teams can target, and do so using web interaction data or product usage data that may live in another data store such as Google BigQuery.
  • Security and Compliance: Detect potential vulnerabilities and compliance gaps while testing in the sandbox, then use this information to address the underlying issues without exposing the live system to risks, safeguarding your data and maintaining trust. Use Data Mask to continue masking your CRM data prior to ingestion into Data Cloud, ensuring critical PII stays secure. 
  • Training and Testing: Train new team members and help familiarize them with Data Cloud in a secure, mirrored environment, ensuring faster time to adoption and value. Enable pre-release access for your key stakeholders, such as a customer service team that will be using Data Cloud data to deliver proactive support, and then iterate fast on their feedback before wider release.

The use cases for exploring and verifying Data Cloud capabilities in a sandbox extend beyond the ones listed here to any scenario you might imagine, with the common thread that a Data Cloud implementation or customization can be tested, extended, and delivered in a secure, isolated environment.

            Promote from sandbox to production, fast

            Once testing is complete and configurations are finalized, promoting the changes from a sandbox to the production environment will be streamlined through tools like Changesets, Salesforce CLI, metadata API, and DevOps Center, all scheduled for Beta availability in July. Once available, admins and developers will benefit from a simplified release management process, allowing teams to deploy validated configurations with greater confidence and ease. This capability not only minimizes deployment risks, but also accelerates the pace at which organizations can respond to changing business needs. 

            Opt into the Beta and start testing today

            All Data Cloud features will be fully functional in sandboxes, although some features might be limited during the Beta. 

            Follow these steps to opt into the Data Cloud in Sandboxes Beta:

            Enable the feature in Data Cloud Setup

            • From Data Cloud Setup, enter Feature in the Quick Find box, then select Feature Manager. See Enable Data Cloud Features.
            • In the Data Cloud In Sandbox panel, click Enable. Note that after you opt into the Beta, you can’t opt out.

            Create a sandbox

            Turn on Data Cloud in the sandbox

              • Log into the sandbox org.

              From Data Cloud Setup, click Get Started. Setup can take a few minutes. See Turn On Data Cloud.

              Welcome to Data Cloud

              Configure connections

              When you create a Data Cloud sandbox, connections are replicated, but the authorization data isn’t, so the connections in the sandbox org have a status of Inactive. You must activate each sandbox connection and provide the authorization information to bring data into the sandbox.

              Conclusion

              The beta release of Data Cloud in Salesforce sandboxes represents a major step forward in our commitment to providing secure, robust, and flexible tools that meet the complex needs of developers and IT teams. Whether you’re configuring a data model or testing new Data Cloud-triggered flows, sandboxes offer a powerful platform for innovation without compromising the stability of your production systems. Stay tuned for further updates, detailed guides, and more as we continue to expand and enhance this exciting capability. 

              Resources 

              About the authors

              Margot Tollefsen is a Product Marketing Manager at Salesforce on the Einstein 1 Platform team, where she leads Developer Experience and broader DevOps messaging and positioning. Alongside her role in product marketing, she is passionate about empowering Indigenous communities with digital tools and technology. 

              William Yeh is a Senior Director of Product Management at Salesforce on the Data Cloud team, where he leads platform integration and extensibility initiatives. He is passionate about building SaaS and data platforms, and empowering admins and developers.

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