Exposing Databricks API in Salesforce

ManojkMohan
Honored Contributor II

‎10-23-2025 01:57 PM - edited ‎10-23-2025 01:59 PM

Use Case:

I want to expose a data bricks API URL in Salesforce, Salesforce will hit that exposed end point every time a record is created and data will be transferred from Salesforce to Databricks

When i try creating a serving end point

ManojkMohan_0-1761252978733.png

I am unable to see Feature Spec Function

Is this the best way to create a databricks end point ? Looking for suggestions on best practices

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mark_ott
Databricks Employee
Databricks Employee

‎10-24-2025 07:07 AM

When integrating Salesforce with Databricks to push data upon record creation, using a serving endpoint is not the most common or optimal approach. Although Databricks Feature Serving endpoints can be used for model or feature APIs, they are primarily designed for real-time inference or feature retrieval, not as general REST ingestion endpoints for Salesforce-originated data.​

The Feature Spec Function you’re missing only appears when using the Databricks Feature Engineering client (databricks-feature-engineering), where you explicitly define a FeatureSpec using Unity Catalog. If you’re not performing real-time feature lookups, you don’t need this setup.​

Recommended Integration Approaches

1. Salesforce → Databricks via API Gateway or Middleware

The most reliable approach is to create a proxy API layer between Salesforce and Databricks rather than exposing Databricks directly.
You can:

  • Create a small Express.js or Flask API that Salesforce calls after a record is created.

  • The middleware forwards data to Databricks via the Databricks REST API or a Delta Live Table ingestion job.​

  • This approach adds resiliency, logging, and retry mechanisms.

2. Salesforce Data Cloud → Databricks (Zero Copy Integration)

If you use Salesforce Data Cloud, use the new Zero Copy Data Sharing integration:

  • Allows bi-directional data sync between Salesforce and Databricks.

  • Eliminates ETL complexity and avoids REST API maintenance.

  • Supports direct access via Iceberg tables without duplication.​

3. Databricks Lakeflow Connect (ETL-based)

Databricks has Lakeflow Connect for ingesting data from Salesforce directly using secure connectors:

  • Ideal for near-real-time or batch synchronization.

  • Handles authentication, schema mapping, and incremental updates natively.​

  • Use this if your goal is data movement rather than live transactional events.

4. Event-driven Integration with Salesforce Platform Events

If you must trigger data transfers upon record creation:

  • Use Salesforce Flow or Apex Trigger to send HTTP POST requests to your middleware API.

  • Middleware calls Databricks REST API endpoints (Jobs or Model Serving).

  • Avoid calling Databricks directly from Salesforce to reduce authentication and timeouts issues.​

Best Practices

  • Avoid exposing Databricks endpoints publicly for direct Salesforce calls.

  • Use service principals and Unity Catalog permissions to secure Databricks endpoints.​

  • For real-time model inference or enrichment, use Feature Serving; for ingesting raw transactional data, use ETL or middleware orchestration.

  • Validate integration through monitoring, retry logic, and API throttling.

In summary, creating a Databricks serving endpoint is not the best way for general data ingestion from Salesforce. The recommended setup is a middleware or Lakeflow Connect integration, with Feature Serving endpoints reserved for machine learning applications.

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