How to Power Data 360 with Code Extension
Code Extension resolves a dilemma an architect faces: the need to operationalize complex data manipulations without leaving the Salesforce trust boundary.
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Key Takeaways
- Major industry investment.
- Traditionally, handling advanced requirements like parsing complex XML, managing encrypted data, or designing custom AI chunking algorithms, led architects to export data to external systems or…
- Moving the data to perform advanced processing on it introduces risks, including rogue code security vulnerabilities, and compliance deviations (for example, GDPR and HIPAA).
What It Means
Context
Traditionally, handling advanced requirements like parsing complex XML, managing encrypted data, or designing custom AI chunking algorithms, led architects to export data to external systems or rely on unmanaged local scripts. Moving the data to perform advanced processing on it introduces risks, including rogue code security vulnerabilities, and compliance deviations (for example, GDPR and HIPAA). Code Extension is a game changer by providing a secure, native Python runtime directly all within Data 360 . What is Code Extension? Code Extension is designed to handle scenarios that no code or low code cannot. They are pro-code capabilities that allow you to transform and process data using native Python execution. As an architect, you must determine the correct execution type based on your specific capabilities and business needs. There are two primary types of extensions available: 1. Code Extension Script (Transforms): These are used to create Batch Data Transforms using custom code. They are ideal for cleaning data, advanced string manipulation, or secure in-platform decrypting web engagement data to match with customer profiles. You can handle complex computations or parsing…
For builders
Traditionally, handling advanced requirements like parsing complex XML, managing encrypted data, or designing custom AI chunking algorithms, led architects to export data to external systems or…
For Builders
Traditionally, handling advanced requirements like parsing complex XML, managing encrypted data, or designing custom AI chunking algorithms, led architects to export data to external systems or…