Rippling Data Cloud: Data Connectors

As part of today's Rippling Data Cloud announcement, we launched Data Connectors, which provide data import capabilities that exceed the standard of standalone ETL products. They automatically preserve and enrich data context, and they wrap your data in the powerful primitives of the Rippling platform. Let's take a look at what makes Data Connectors unique inside Rippling.

What are Rippling Data Connectors? 

Rippling Data Connectors are integrations to third-party systems that move data into Rippling. Managed Connectors are integrations built and maintained by Rippling on a customer's behalf. Custom Connectors can be built and maintained by customers to integrate to any system that has a supported interface. Rippling Data Connectors also include Zero Copy, for easy integration with Snowflake, with support for Iceberg, BigQuery and other systems forthcoming. Lastly, customers can also manually import CSVs.

At launch, Rippling provides managed connectors to 22 third-party systems, including Salesforce, Github, Square, and Greenhouse.

Automatically preserve and enrich context

Traditional ETL products lift and shift data from a business system into a data warehouse. But data models, joins, permissions, and metadata must be handled manually on the other side before the data becomes useful for analysis. 

Rippling Data Cloud automatically preserves worker identity associations across a customer’s data. When external data lands in Rippling, the software identifies fields that reference users: email addresses, employee IDs, usernames, and display names. It joins them to the corresponding Rippling identity profile, so that analysts and business users don’t have to configure those joins themselves. It uses the same identity-resolution technology that powers Rippling IT’s Identity and Access Management software across the hundreds of business software systems our customers use.

Raw data is enriched with additional context about the domain, the source, and the actual data that is imported per customer account

Data Cloud similarly maintains references between objects within the third-party system, like the link between a support case and its comments, or a sales opportunity and its parent account.

Permissions are automatic, even as your organization changes

Permissions are powered by identities and the relationships among those identities: who reports to whom and who is a member of what department. Almost all business data has a worker identity association: Github PRs have an author, point-of-sale transactions have a cashier, helpdesk tickets have an assignee.

Inside Rippling, employees can see their own pull requests, transactions, or tickets, and all managers can automatically see that same data for their team. When teams inevitably reorganize, permissions automatically adjust so the right people have access to the right data. In essence, the system manages data permissions “for free.”

Data arrives with context

When data crosses system boundaries, it almost always sheds context. A CRM opportunity becomes a row. A Jira ticket becomes a row. A candidate record from an ATS becomes a row. The data may arrive, but the meaning around it often gets stripped away: what the object represents, how it relates to other objects, which fields matter, and how the source system expects the data to be used. But that context is exactly what AI needs to answer questions correctly.

Rippling Data Connectors are designed to bring in source-system context, not just rows of data. For a Hubspot connector, for example, Rippling can read the Hubspot API docs, understand the data contract, and derive how Hubspot objects relate to each other and how they should map into Rippling before the connector is generated. That means a Deal is not treated as a flat row. It can be understood as something connected to Companies, Contacts, Orders,  Products, and lifecycle events, so Rippling can preserve the commercial context around the deal rather than importing only its raw data.

Once the data lands in Rippling, that source-specific context is enriched with what Rippling learns from the customer’s own data: the fields they import, custom fields and objects, known relationships, field descriptions, usage patterns, and signals like sparse or stale fields. So when someone asks why one segment has higher win rates than another, Rippling AI starts with connected sales objects, not just a pile of column names.

This context also flows into Data Catalog, where teams can discover connected objects, inspect field descriptions, understand relationships, and see how data is used across Dashboards, Reports, AI, and Transformations.

From rows in a table to highly capable data objects

Data imported into Rippling benefits from the capabilities we’ve built around first-party application data for the last ten years. It works with Custom Applications, can be analyzed in reports and dashboards, and can trigger workflows. For example:

• A sales manager can browse Salesforce opportunities inside Rippling, and open related records like the owner, account, and related activity without jumping between systems.

• An Engineering manager can analyze the cost in AI tokens per Github pull request across their team, compare across employees, and identify opportunities to cut costs.

• When a Zendesk case for a strategic customer is escalated to Sev-1, Rippling can notify the support agent’s manager, alert the account owner, and create a follow-up task for the product team.

Every custom object gets a fully customizable detail page view for a richer app experience

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Includes the full foundations of ETL

No data solution would be complete without delivering on the fundamental capabilities of ETL. Data Connectors allow you to:

• Configure exactly which tables and fields you want to import into Rippling

• Perform incremental syncs with automatic rate limit throttling

• Adjust sync schedules, so you can control how fresh your data is

• View a detailed history of every data sync, including what succeeded or failed down to the record and field

• Store credentials securely

• Automate schema updates so that when fields change or new ones get created in your source system they’re available in Rippling on the next sync

Because Rippling provides end-to-end Lineage, tracing issues to specific connectors, syncs, and owners is a tractable problem.

Custom Connectors

In addition to the Managed Connector library provided by Rippling, Custom Connectors allow any customer to import data from APIs for any business system they use. Custom Connectors run on Rippling infrastructure, so no third-party service is necessary. They include standardized concepts like pagination and incremental syncs to make the system efficient, with observability that’s on par with Managed Connectors. Customers can set them up directly in the UI or partner with a Forward Deployed Engineer to build out an entire Rippling Solution.

Conclusion

Rippling Data Connectors make it easy to bring your operational data into Rippling and join it on worker identity, which unlocks the power of Rippling AI and Rippling Dashboards. They simplify data integrations, enhance data context, and preserve lineage. The system delivers automatic, hierarchy-based permission management that updates as your team evolves, while enabling Rippling AI to intelligently interpret and join data without manual configuration. They’re the backbone of upgraded analytical insights for your business with Rippling Data Cloud.