Rippling Data Cloud: Transformations. Jun 25, 2026 As part of today's Rippling Data Cloud announcement, Rippling launched Transformations, which allows a company to standardize and control data definitions and reuse these components across their business. What is Transformations? Rippling Transformations is a set of tools that allows users to create governed datasets from source business data. Analysts can write SQL directly, and less technical users can work with Rippling AI to ask questions, refine definitions and logic, and save the results. Results can be shared to drive consistent data interpretation within your organization. This capability relies on Data Cloud's underlying data management capabilities, like Data Connectors and History, and it can be accessed anywhere you do work with your data, like Dashboards, Custom Apps or Rippling AI. This capability is especially valuable for comparative analyses, such as comparing the bookings of two or more sales teams, or how their win rate changed over time. Analyses like these are only reliable if bookings and win rate are measured consistently over time. Transformations makes this easy by "locking in" a clear definition, and ensures that these definitions show up everywhere that analysts and business users are working with your data. Transformations save business-specific definitions to ensure everyone gets a consistent answer Transformations is more important than ever in the era of AI, because users are increasingly "free feeding" on analysis themselves. Without governed definitions, users will unwittingly run calculations with definitions that differ slightly from the prior run, because the AI quietly changed its opinion on how to calculate a particular value. And when two team members get in front of their boss to debate a decision, they're arguing from calculations that are inconsistent. (Awkward.) Data governance is better inside Rippling. Because Rippling Data Cloud is an all-in-one analytical environment, Transformations is more capable, and more readily available to all users. For these reasons, it enjoys broader adoption, which is the toughest part of driving data governance in your business. Rippling-specific SQL functions in Transformations. Transformations support the standard SQL patterns analysts expect: joins, unions, window functions, conditional logic, and post-aggregation calculations. Rippling then extends SQL with functions that would be painful to recreate in a standalone warehouse or BI tool. And because all business questions eventually ask "who," primitives about your organization turn out to be useful in almost every analysis. The ORG function lets you query reporting chains and org hierarchies directly. History functions like VALUEASOF and DATEOFCHANGE let you evaluate employee and org context as it existed at a point in time. Rippling even handles currency normalization, so analysts and AI agents do not accidentally aggregate values across currencies without the right conversion logic. Consistent, automatic permissions. In a traditional stack, transformation logic and access control often live in different places. You model the data in one system, then recreate permissions in a BI tool or data warehouse. In Rippling, Transformations is governed by the same permissions model as the rest of the platform, so reusable datasets can respect the entitlements of a given user. Outputs that can become operational. A Transformation does not have to stop as a table for analytics. It can write back to the warehouse, which means the result can trigger workflows, appear on data detail pages, and become the data layer for custom apps. For example, some Rippling customers have set up their own logic for how restaurant tips should get pooled via Transformations, and then they are able to give every employee visibility into their own tip earnings in a Custom Application. That exact same data is used to automatically include those amounts in the next payroll run. Another customer used a Transformation to combine CRM, support, product usage, and account-owner data, then publish a Customer Risk object that drives dashboards, renewal workflows, and account review pages. Practical applications of Transformations. A retail district manager can create a daily store performance dataset that combines point-of-sale data, scheduled labor, clock-ins, overtime, returns, and inventory exceptions. Without a Transformation, each district manager might calculate "sales per labor hour" slightly differently: one includes returns, another excludes manager hours, another forgets to adjust for missed clock-ins. With Transformations, those choices are defined once, so that every store and district looks at the same definition. Store managers can track performance for their own locations, district managers can compare across stores, and Rippling AI can answer questions from the same governed dataset. Because it lives in Rippling, the dataset can also trigger workflows when a store is trending toward overtime or missed-break exposure. A telemedicine provider can create a capacity dataset that combines patient volume, provider schedules, credentialing status, and state licensure data managed in a Rippling Custom App. Without a Transformation, capacity planning often becomes a spreadsheet exercise where someone manually reconciles the supply of licensed providers against the needs of people in a given location. With Transformations, the rules are captured once: which appointment types count toward demand, which providers are eligible in each location, how cancellations affect capacity, and when a region should be considered under-covered. The resulting dataset can power dashboards, AI answers, and workflows that alert operations when new headcount needs to be opened or schedules need to be adjusted. A professional services firm can create a project staffing and margin dataset that combines time tracking, billing rates, project budgets, employee skills, utilization targets, and PTO. Without a Transformation, every project review risks using a different definition of margin or availability: one team includes subcontractor costs, another ignores non-billable management time, another treats someone as available even though they are on PTO next week. With Transformations, those assumptions become shared logic. Leaders can inspect project health consistently, AI can answer staffing questions using the same definitions, and the output can feed a custom staffing app that helps managers assign the right people before projects fall behind. Write the logic once. The definitions that matter most to your business should not live in a spreadsheet, a one-off SQL query, a dashboard formula, or a prompt that someone has to remember to reuse. Transformations give that logic a governed home: written with SQL, assisted by AI, enriched by Rippling's employee graph and history, and available everywhere the business needs it. That is the larger promise of Rippling Data Cloud. It does not just bring business data together. It gives teams a way to define what that data means, reuse those definitions consistently, and turn the result into action. Disclaimer Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions. Director of Product Management Suraj Savalia is Director of Product Management at Rippling, where he leads Data Platform and AI products. He has spent over a decade building B2B software, from early-stage startups to enterprise platforms, with a focus on data, analytics, and AI.

Rippling Data Cloud: Zero Copy for Snowflake. Jun 25, 2026 As part of today's Rippling Data Cloud announcement, Rippling is launching Zero Copy Query Federation for Snowflake, giving companies a direct way to bring business data into Rippling Data Cloud without having to replicate it. With Zero Copy, you can connect data you have already replicated from systems like sales, finance, support, product, and engineering to the employee data already in Rippling. What is Zero Copy for Snowflake? Zero Copy is a native data connection between Snowflake and Rippling Data Cloud. Data is continuously available as first-class objects inside Rippling, without the overhead of ETL pipelines. Zero Copy is an alternative to using traditional Data Connectors, which offer different benefits. Whereas Data Connectors can bring in rich metadata from the source system itself, Zero Copy connections build context directly from the warehouse by analyzing query history, identifying which fields are actively maintained (or deprecated), and sampling real values from each table and field. The benefits of Rippling-on-top. Using Rippling AI to analyze data via Zero Copy is superior to analyzing that data directly inside Snowflake. A generic BI/AI layer can query data, but it lacks the critical context of Rippling's worker identities, permissions, historical employment context, and the semantic understanding of workforce fields. Marrying this with sales, finance, or support data allows you to answer real business questions that begin with "who." It also democratizes and governs access to data in your warehouse. HRBPs, finance partners, managers, and executives can ask natural-language questions that combine warehouse data with Rippling data without needing Snowflake credentials, schema knowledge, or to make a data-team request. Other reasons this approach might make sense for your business include: * Rippling can now connect to warehouse data in-place. With a zero copy connection to Snowflake, Rippling can query existing warehouse data without duplicating, migrating, or re-platforming it. These objects appear in the Data Catalog and can be used in reports, dashboards, transformations, and AI queries, just like native Rippling data. * Business data is joined to the worker identities. Companies can connect revenue, product usage, support, finance, or engineering data to employees, teams, managers, departments, locations, and cost centers, which is foundational to good analysis. * Context is enriched. Although Data Connectors do more to enhance the context of data inside Rippling, data available via Zero Copy still gets query-history context, sample values, and information about which fields are most or least populated. * Rippling applies org-aware permissions to warehouse data. Access can be scoped by role, department, reporting line, or permission profile, and updates automatically as people change jobs or managers, even though the data remains in Snowflake. The result is a new way to use the business data you already have: not as isolated tables in a warehouse, but as employee-aware, permission-aware objects inside Rippling Data Cloud. Your warehouse continues to be the source of truth for business activity; Rippling adds the worker context needed to understand the "who" questions. Disclaimer Rippling and its affiliates do not provide tax, accounting, or legal advice. This material has been prepared for informational purposes only, and is not intended to provide or be relied on for tax, accounting, or legal advice. You should consult your own tax, accounting, and legal advisors before engaging in any related activities or transactions. Director of Product Management Suraj Savalia is Director of Product Management at Rippling, where he leads Data Platform and AI products. He has spent over a decade building B2B software, from early-stage startups to enterprise platforms, with a focus on data, analytics, and AI.