What is the difference between DataRaptor & DataRaptor Turbo?

DataRaptor and DataRaptor Turbo are both powerful tools provided by Salesforce Industries (formerly Vlocity) that serve as integral components in managing and transforming data within the Salesforce platform. They are designed to simplify interactions with Salesforce Objects and external data sources, enhancing the capabilities of OmniScripts, Integration Procedures, and other Salesforce Industries functionalities. Here’s a breakdown of the key differences between DataRaptor and DataRaptor Turbo:


  • Purpose: DataRaptors are primarily used for extracting, transforming, and loading (ETL) data. They can be utilized to prepare data for display in OmniScripts, for data migration tasks, and to facilitate data integration between Salesforce and external systems.
  • Functionality:
    • Extract: Query Salesforce data or receive input data.
    • Transform: Manipulate data fields, including formatting, calculations, and setting default values.
    • Load: Create, update, or upsert Salesforce records based on the transformed data.
  • Configuration: Configured through a JSON-based structure where you define the inputs, transformations, and outputs.
  • Flexibility: Offers a wide range of transformation capabilities, making it suitable for complex data manipulation tasks.
  • Performance: While efficient, the performance can be affected by the complexity and volume of data being processed.

DataRaptor Turbo

  • Purpose: DataRaptor Turbo extends the capabilities of standard DataRaptors by offering enhanced performance and efficiency, particularly for high-volume data operations. It’s optimized for speed and is primarily used in scenarios where performance is critical.
  • Functionality:
    • Focused on high-speed operations: Optimized for reading and writing Salesforce data quickly.
    • Batch processing: Enhanced capabilities for handling large volumes of data in batch operations, significantly reducing processing time.
  • Configuration: While it also uses a JSON-based configuration, the focus is on minimizing processing overhead for faster execution.
  • Flexibility: While highly efficient, the use cases might be more specific due to its optimization for speed over the breadth of transformation capabilities.
  • Performance: Designed to outperform standard DataRaptors in terms of execution speed, particularly beneficial for bulk data operations and scenarios requiring rapid data throughput.


The main difference between DataRaptor and DataRaptor Turbo lies in their intended use cases and performance characteristics. DataRaptor is a versatile tool for ETL tasks that require complex data transformations and manipulations. On the other hand, DataRaptor Turbo is optimized for speed, making it the preferred choice for high-volume data operations where performance is a critical factor. The selection between DataRaptor and DataRaptor Turbo depends on the specific requirements of the task at hand, such as the complexity of data transformation needed versus the importance of processing speed.

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