--- sourceDocument: Brazil Workflow Data Fabric sourceDocumentLink: https://www.servicenow.com/docs/r/integrate-applications Release : - brazil ft:locale : - en-US ft:publication_title : - Brazil Workflow Data Fabric ft:clusterId : - crint bundleId : - crint workflow : - Creator --- # Robust Import Set Transformers # Robust Import Set Transformers {#ariaid-title1} Release version: Brazil Updated September 10, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of Robust Import Set Transformers Robust Import Set Transformers provide an advanced method for extracting, transforming, and loading (ETL) data into one or more target tables within ServiceNow. Unlike traditional transform maps, which map data from a staging table to a single target table and perform both transformation and processing, robust import set transformers separate these functions. This separation offers greater flexibility and efficiency, particularly when loading data into multiple target tables. Show full answer Show less ## Key Features * **Separation of Transform and Processing:** The Robust Transform Engine (RTE) enables data extraction into intermediary structures before transformation and loading, improving manageability and customization. * **Batch Processing:** Records are processed in batches to optimize performance during data integration. * **Efficiency Across Multiple Targets:** Robust import set transformers require only one read operation to prepare data for multiple target tables, unlike transform maps which need separate reads per target table. * **Extract Transform Load (ETL) Definitions:** Users can define entities (similar to tables), entity fields, mappings between entities, field mappings, and operations to control how data moves from source to target tables. * **Support for Nested Data Structures:** Since the Paris release, ETL definitions handle complex JSON/XML payloads with nested objects and arrays within a single column, enabling sophisticated data integration scenarios. ## Practical Benefits for ServiceNow Customers * Improved data integration performance when working with multiple target tables by reducing redundant data reads. * Greater flexibility in defining how data is extracted, transformed, and loaded, allowing for complex mappings and operations. * Ability to handle complex nested data payloads, expanding integration possibilities with modern data sources. * Simplified management of data transformation workflows by separating transformation logic from processing tasks. ## Next Steps To leverage these capabilities, ServiceNow customers should create robust import set transformers and corresponding robust transform definitions. Additionally, defining ETL definitions allows precise control over data entity mappings and operations. Resources are available to guide through creating robust import set transformers and ETL definitions to maximize these benefits. Use robust import set transformers instead of transform maps if you want to extract, transform, and load data to one or more target tables. ## Robust import set transformers versus transform maps {#robust-import-set-transformers__section_pwg_d52_hjb} Transform maps define the mapping from imported data stored in a staging table to a single target table in the ServiceNow AI Platform. Transform maps also insert data into target tables, performing both transform and processing functions. You can define multiple table mappings with multiple transform maps. The Robust Transform Engine (RTE) and the robust import set transformer separate the transform and processing functions, providing a more flexible alternative to transform maps. The robust import set transformer allows you to extract data from a source table into an intermediary data structure. You can transform the data as desired and then load that data to one or more target tables. Records are processed as batches to enhance performance. With transform maps, if you want data from the source table to go to three different target tables, you must create three separate transform maps. Each transform map parses the data separately, which results in three separate read operations. By contrast, the robust import set transformer requires only a single read operation to prepare the data for three target tables. The robust import set transformer is more efficient, especially when dealing with multiple target tables. ## Extract Transform Load (ETL) {#robust-import-set-transformers__section_crv_zt2_hjb} When you use the robust import set transformer, Extract Transform Load (ETL) functionality transfers imported data to target tables. You define how the data is extracted, transformed, and loaded to one or more target tables. You can use ETL definitions to do the following: * Define entities (an abstraction similar to tables). * Define entity fields (an abstraction similar to table fields). * Define mapping between entities, and optionally designate whether a specific mapping should be ignored during data integration. * Define entity field mappings. * Define entity operations. {#robust-import-set-transformers__ul_m2g_p4m_kjb} For an overview of ETL definitions, see [Extract Transform Load (ETL) definition overview](https://www.servicenow.com/docs/~TePkYTtQM3dPDXs0d9png "ETL definitions extract data from a source table, transform the data as desired, and load the data into one or more target tables. ETL definitions also support nested data structures."). For a step-by-step guide on how to create ETL definitions, see [Create Extract Transform Load (ETL) definitions](https://www.servicenow.com/docs/rFf8Ke~eAnHwx0wr~EAZ3w "Create ETL definitions to extract data from a source table, transform the data as desired, and load the data into multiple target tables. ETL definitions also support nested data structures."). As of the Paris release, ETL definitions also support nested data structures such as the following when the Data source has JSON/XML payloads in a single column. { "records":[ { "network":{ "location":"San Diego", "computers":[ { "id":"C100", "os":"Mac", "disks":[ { "size":"200GB", "type":"SSD" }, { "size":"1TB", "type":"Magnetic" }, { "size":"1TB", "type":"Magnetic" } ] }, { "id":"C200", "os":"Windows", "disks":[ { "size":"5TB", "type":"Magnetic" } ] } ] } } ] } **Related tasks** * [Create a robust import set transformer](https://www.servicenow.com/docs/xMQuZsJohXhQ_pEsT4CcoA "Define how information is sent from the source table to target tables via a Robust Transform Engine (RTE).") * [Create robust transform definitions](https://www.servicenow.com/docs/9KioylalnRbdsaq1TXpdoQ "Specify your own Robust Transform Engine (RTE) to transfer data from a source table to a target table.") * [Create Extract Transform Load (ETL) definitions](https://www.servicenow.com/docs/rFf8Ke~eAnHwx0wr~EAZ3w "Create ETL definitions to extract data from a source table, transform the data as desired, and load the data into multiple target tables. ETL definitions also support nested data structures.")