Seamless Data Integration with Salesforce OmniStudio DataRaptor

Introduction to Salesforce OmniStudio DataRaptor/Data Mapper

Salesforce OmniStudio is a suite of tools designed to streamline digital interactions and workflows. A crucial component within this suite is the DataRaptor, also known as Data Mapper. DataRaptor is a declarative data integration tool that allows users to read, transform, and write Salesforce data seamlessly. It simplifies data extraction and manipulation, making it essential for anyone looking to build efficient and powerful applications within Salesforce.

Specifications and Key Features

Specifications

• Declarative Tool: DataRaptor is a point-and-click tool that requires no coding.
• Integration: Seamlessly integrates with Salesforce data, allowing for easy data retrieval and manipulation.
• Flexibility: Supports complex data transformation and mapping.

Key Features

• Data Extraction: Extracts data from Salesforce objects or external systems.
• Data Transformation: Performs complex transformations on the extracted data.
• Data Loading: Loads data into Salesforce objects.
• Declarative Mapping: Allows for intuitive data mapping through a user-friendly interface.
• Data Operations: Supports CRUD (Create, Read, Update, Delete) operations.

Types of DataRaptors

Salesforce OmniStudio offers four main types of DataRaptors: Extract, Load, Transform, and Turbo Extract. Each serves a unique purpose in data management and manipulation, allowing users to efficiently handle data operations within Salesforce. Here’s a detailed explanation of each type:

Dataraptor

1. DataRaptor Extract

Purpose: DataRaptor Extract is designed to retrieve data from Salesforce objects. It allows for detailed and complex data extraction, including the ability to filter, sort, and transform data using formulas and logic.

   Key Features:

• Data Retrieval: Extracts data from single or multiple Salesforce objects.
• Filtering and Sorting: Allows filtering of data based on specific criteria and sorting the results.
• Complex Transformations: Supports applying formulas and logic to transform the extracted data.
• Field Mapping: Enables detailed mapping of extracted fields to the output structure.
• Nested Data: Can extract related data from parent and child objects.

   Use Cases:

• Creating personalized reports by extracting customer information along with related transactions.
• Retrieving data for display in user interfaces, such as pulling account details and associated contacts.
• Extracting data for integration with external systems.

   Example: Extracting customer data along with their recent transactions, applying a formula to calculate the total transaction amount, and formatting the data for a report.

2. DataRaptor Load

Purpose: DataRaptor Load is used to insert, update, or delete data in Salesforce objects. It is ideal for scenarios where data needs to be loaded into Salesforce from external sources or after processing within the system.

   Key Features:

• Data Loading: Performs insert, update, or delete operations on Salesforce objects.
• Multiple Objects: Supports loading data into multiple objects, including related records.
• Field Mapping: Allows mapping of input data fields to Salesforce object fields.
• Error Handling: Provides options for error handling and data validation.

   Use Cases:

• Loading data from external systems into Salesforce for data synchronization.
• Updating existing records with new information from processed datasets.
• Deleting obsolete or incorrect records as part of data cleanup processes.

   Example: Loading a batch of new customer records into Salesforce, updating their contact information, and associating them with existing accounts.

3. DataRaptor Transform

Purpose: DataRaptor Transform is designed for complex data transformations. It allows users to apply formulas, logic, and data manipulations to transform the data before loading it into Salesforce or sending it to another system.

   Key Features:

• Complex Transformations: Supports applying complex formulas and logic to manipulate data.
• Data Enrichment: Can enrich data by combining fields, performing calculations, and applying business rules.
• Intermediate Processing: Acts as an intermediary step between data extraction and loading, transforming data as needed.

   Use Cases:

• Transforming raw data into a structured format suitable for reporting or analysis.
• Applying business rules to data before loading it into Salesforce.
• Combining and enriching data from multiple sources to create a comprehensive dataset.

   Example: Transforming raw sales data by calculating total sales amounts, applying discounts, and formatting the data for a financial report.

4. DataRaptor Turbo Extract

Purpose: DataRaptor Turbo Extract is optimized for high-speed data retrieval. It is designed to handle large volumes of data efficiently, making it suitable for scenarios where performance is critical.

Specify a Key/Value pair in the Input Parameters panel (1), by entering AccountId for the Key and an account’s RecordId for the Value (we explain how to find the RecordId next). When you execute the Omnistudio Data Mapper Turbo Extract, the Response pane (2) returns results to confirm it is extracting data correctly.

All records in Salesforce have a unique RecordId. To find a RecordId for an account, simply open any Account record in your org, and copy the RecordId from the URL.

   Key Features:

• High Performance: Optimized for fast data retrieval, especially for large datasets.
• Simplified Transformations: Limited support for complex transformations to ensure faster performance.
• Bulk Data Handling: Efficiently processes large volumes of data.
• Minimal Overhead: Focuses on essential extraction tasks, reducing processing overhead.

   Use Cases:

• Retrieving large datasets for data migration or integration tasks.
• Extracting bulk data for analytics or reporting purposes.
• Scenarios where quick data retrieval is more important than complex transformations.

   Example: Extracting all account records for migration to a data warehouse, where the primary requirement is speed rather than data transformation.

Omnistudio Data Mapper Extract, Omnistudio Data Mapper Load, and Omnistudio Data Mapper Transform also do the following.

• Trim, map, restructure, and transform data in JSON, XML, or Custom Schema.
• Use formulas and functions to transform data.

Difference Between DataRaptor Extract and DataRaptor Turbo Extract

Choosing Between DataRaptor Extract and Turbo Extract

When to Use DataRaptor Extract:

• When you need to perform complex data transformations.
• When extracting data from related objects with detailed field mappings.
• When the volume of data is moderate and performance is not the primary concern.

     Example:

• Extracting customer data along with their recent transactions and applying a formula to calculate the total transaction amount.

When to Use DataRaptor Turbo Extract:

• When dealing with large volumes of data where performance is critical.
• When the primary requirement is to retrieve data quickly with minimal transformations.
• When the focus is on bulk data extraction rather than detailed field-level transformations.

     Example:

• Extracting all account records for migration to a data warehouse, where the primary requirement is speed rather than data transformation.

How to Create a DataRaptor

Refer to Signup to your Omnistudio Org:  Supercharge customer journeys with Salesforce Omnistudio

Step-by-Step Guide

Extract Dataraptor

1. Navigate to OmniStudio DataRaptors:
   • In Salesforce, go to OmniStudio -> DataRaptors.
2. Create a New DataRaptor:
   • Click on “New DataRaptor.”
   • Select the type of DataRaptor you need (Extract, Load, Transform, Turbo Extract).
3. Define the DataRaptor:
   • Name: Give your DataRaptor a meaningful name.
   • Objects: Select the Salesforce objects you want to work with.
   • Fields: Choose the fields you need to extract, transform, or load.
4. Set Up Data Mapping:
   • Input Mapping: Map input data fields to the corresponding Salesforce object fields.
   • Output Mapping: Define how the output data should be structured.
5. Configure Transformations (if applicable):
   • Use formulas and functions to transform data as needed.
6. Test the DataRaptor:
   • Use the “Test” feature to ensure your DataRaptor works as expected.
   • Verify the output to make sure the data is correctly extracted, transformed, or loaded.
7. Deploy the DataRaptor:
   • Once tested, deploy your DataRaptor to make it available for use in your applications.

Where to Use DataRaptors in OmniStudio

Salesforce OmniStudio offers powerful tools to create guided user experiences, automate business processes, and manage data efficiently. DataRaptors are integral to OmniStudio for handling data operations. Here are the main areas where DataRaptors can be used within OmniStudio:

1. OmniScripts

   OmniScripts are interactive guided processes that help users complete tasks by navigating through a series of steps. DataRaptors play a crucial role in OmniScripts by enabling data extraction, transformation, and loading at various points in the process.

   Use Cases:

• Data Retrieval: Use DataRaptor Extract to pull necessary data from Salesforce to display in the script.
• Data Submission: Use DataRaptor Load to save user inputs back into Salesforce.
• Intermediate Processing: Use DataRaptor Transform to apply business logic to data before displaying it to the user or saving it.

   Example: In a customer service OmniScript, DataRaptor Extract can retrieve customer details based on an inputted account number, and DataRaptor Load can save the user’s updated contact information.

2. FlexCards

   FlexCards are UI components used to display data and actions in a visually appealing format. They can be embedded in Lightning pages, Community pages, or other Salesforce components.

   Use Cases:

• Data Display: Use DataRaptor Extract to pull data that needs to be displayed in FlexCards.
• Dynamic Content: Use DataRaptor Transform to format and manipulate the data before displaying it.

   Example: A FlexCard displaying a summary of account information might use a DataRaptor Extract to pull account details and recent transactions, transforming the data to show it in a compact, user-friendly format.

3. Integration Procedures

   Integration Procedures are declarative, server-side processes that execute multiple actions in a single transaction. They are used to orchestrate data from different sources and perform complex data manipulations.

   Use Cases:

• Data Aggregation: Use DataRaptor Extract to pull data from various Salesforce objects.
• Data Transformation: Use DataRaptor Transform to manipulate and combine data from different sources.
• Data Loading: Use DataRaptor Load to save aggregated and transformed data back to Salesforce.

   Example: An Integration Procedure might use DataRaptor Extract to retrieve data from multiple related objects, DataRaptor Transform to apply business rules, and DataRaptor Load to save the final data set to a different object.

4. OmniStudio Actions

   OmniStudio Actions are reusable actions that can be added to FlexCards and OmniScripts to perform operations such as data extraction, data transformation, and data loading.

   Use Cases:

• Reusable Components: Create standardized DataRaptor actions that can be used across multiple OmniScripts and FlexCards.

   Example: An action for extracting and displaying a list of active cases for a customer, which can be reused in various FlexCards and OmniScripts.

For more information please visit: Trailhead modules

Best Practices for Using DataRaptors

Design

• Simplify Mappings: Keep mappings as simple and straightforward as possible.
• Reuse Components: Reuse existing DataRaptors wherever possible to avoid redundancy.
• Limit Data Volume: Extract only the necessary data to improve performance.
• The Omnistudio Data Mapper naming convention needs to be in the lowerCamelCase.

Performance

• Optimize Queries: Use filters and limits to reduce the amount of data processed.
• Use Turbo Extract: For large datasets, use DataRaptor Turbo Extract for better performance.
• Batch Processing: Process data in batches to manage large volumes efficiently.

Maintenance

• Version Control: Maintain versions of DataRaptors to track changes and roll back if needed.
• Documentation: Document your DataRaptors thoroughly to make maintenance easier.
• Testing: Regularly test DataRaptors, especially after any changes, to ensure they continue to function correctly.

Conclusion

Salesforce OmniStudio DataRaptors are powerful tools that simplify data integration and transformation within Salesforce. By following best practices and leveraging the capabilities of different types of DataRaptors, you can create efficient and maintainable data workflows. Whether you are extracting data for analysis or transforming data for load operations, DataRaptors provide the flexibility and power needed to handle complex data scenarios.

By understanding the specifications, key features, and best practices outlined in this guide, you can effectively utilize DataRaptors in your Salesforce environment to enhance data operations and drive better business outcomes.

Related Posts:

1.Supercharge customer journeys with Salesforce Omnistudio

2.FlexCards in Salesforce OmniStudio: A Beginner’s Guide

3.Mastering Salesforce OmniScript: A Comprehensive Guide to Streamlined Workflows