OmniStudio is a powerful platform that enables businesses to create custom Industry Cloud applications by combining various services, components, and data model objects. One of the essential tools in OmniStudio is DataRaptor, which is used to Transfer and Transform Data between Salesforce and other tools in the platform.
DataRaptor in OmniStudio is an advanced data integration and transformation tool that enables businesses to streamline data processes and extract. With its powerful features and intuitive interface, DataRaptor provides businesses with a Comprehensive Solution for Data Processing.
Another significant advantage of DataRaptor OmniStudio is its Automation capabilities. With its Drag-and-Drop Interface, businesses can easily create data integration and transformation workflows, which can be automated to save time and resources. This enables businesses to focus on analyzing data and extracting valuable insights, rather than spending time on manual data processing tasks.
DataRaptor allows us to map salesforce data for reading, transforming and writing data.
It comes in Four Types:
- Turbo Extract
Turbo Extract is used to read data from a single Salesforce object type, while Extract is used to read data from multiple objects and output it in JSON or XML format with complex field mappings. Transform is used to perform intermediate data transformations, while Load is used to insert/update Salesforce data from JSON or XML input.
Let’s take a closer look at each type of DataRaptor in OmniStudio and its uses:
The Turbo Extract DataRaptor is used to read data from a single Salesforce object type and related objects. It supports the selection of specific fields to include in the output. However, complex field mappings and formulas are not supported. Turbo Extract is ideal for quickly extracting data from Salesforce and is useful when data extraction speed is a priority. It is a great tool for generating reports and data snapshots that can be used for various purposes like analyzing data trends, identifying opportunities, or identifying potential problems.
The Extract DataRaptor is used to read data from one or more Salesforce objects and output it in JSON or XML format. It supports complex field mappings and formulas, making it ideal for complex data extraction scenarios. The extracted data can be used for various purposes like creating custom reports, building dashboards, or integrating data with other systems.
The Manufacturer’s Guide to Marketing Through Partner Enablement in 2021
Learn how partnerships allow manufacturers to scale revenue growth beyond what’s possible with direct sales alone.
The Transform DataRaptor is used for intermediate data transformations without reading or writing to Salesforce. It supports formulas and is ideal for transforming data to suit specific business needs. The transformed data can be used for various purposes like data analytics, data visualization, and custom reporting.
The Load DataRaptor is used to update Salesforce data from JSON or XML input. It supports formulas and is ideal for batch data updates or bulk data loading scenarios. It is a great tool for updating or inserting large amounts of data into Salesforce.
Now let us have a quick look on how to Create Your First DataRaptor.
- Login to your Vlocity org.
- Navigate to the “All Tabs” menu.
- Navigate to “OmniStudio DataRaptor”
- Click on “New”
- Provide the necessary details based on your requirements.
- Click on Save and Congratulations, you have successfully created your first Extract DataRaptor.
The four types of DataRaptor in OmniStudio each have their own unique purpose and usage scenarios. Turbo Extract is ideal for quickly extracting data from a single Salesforce object type. Extract is ideal for complex data extraction scenarios. Transform is ideal for intermediate data transformations, and Load is ideal for batch data updates or bulk data loading scenarios. By using these different types of DataRaptor, businesses can streamline their data management processes and make better decisions based on accurate and relevant data.