Ingest data from Salesforce

Learn how to ingest data from Salesforce into Databricks using Lakeflow Connect.

Requirements

To create an ingestion pipeline, you must first meet the following requirements:
- Your workspace must be enabled for Unity Catalog.
- Serverless compute must be enabled for your workspace. See Serverless compute requirements.
- If you plan to create a new connection: You must have CREATE CONNECTION privileges on the metastore. See Manage privileges in Unity Catalog.
  
  If the connector supports UI-based pipeline authoring, an admin can create the connection and the pipeline at the same time by completing the steps on this page. However, if the users who create pipelines use API-based pipeline authoring or are non-admin users, an admin must first create the connection in Catalog Explorer. See Connect to managed ingestion sources.
- If you plan to use an existing connection: You must have USE CONNECTION privileges or ALL PRIVILEGES on the connection object.
- You must have USE CATALOG privileges on the target catalog.
- You must have USE SCHEMA and CREATE TABLE privileges on an existing schema or CREATE SCHEMA privileges on the target catalog.
Salesforce applies usage restrictions to connected apps. The permissions in the following table are required for a successful first-time authentication. If you lack these permissions, Salesforce blocks the connection and requires an admin to install the Databricks connected app.

Condition
Required permission
API Access Control is enabled.
Customize Application and either Modify All Data or Manage Connected Apps
API Access Control is not enabled.
Approve Uninstalled Connected Apps

For background, see Prepare for Connected App Usage Restrictions Change in the Salesforce documentation.
To ingest from Salesforce, the following is recommended:
Create a Salesforce user that Databricks can use to retrieve data. Make sure that the user has API access and access to all of the objects that you plan to ingest.

Condition	Required permission
API Access Control is enabled.	`Customize Application` and either `Modify All Data` or `Manage Connected Apps`
API Access Control is not enabled.	`Approve Uninstalled Connected Apps`

Create an ingestion pipeline

Beta

You can filter rows during ingestion to improve performance and reduce data duplication. See Select rows to ingest.

Databricks UI
Databricks Asset Bundles

In the sidebar of the Databricks workspace, click Data Ingestion.
On the Add data page, under Databricks connectors, click Salesforce.
On the Connection page of the ingestion wizard, select the connection that stores your Salesforce access credentials. If you have the CREATE CONNECTION privilege on the metastore, you can click Create connection to create a new connection with the authentication details in Salesforce.
Click Next.
On the Ingestion setup page, enter a unique name for the pipeline.
Select a catalog and a schema to write event logs to. If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, you can click Create schema in the drop-down menu to create a new schema.
Click Create pipeline and continue.
On the Source page, select the tables to ingest. If you select All tables, all existing and future tables in the source schema will be ingested.
Click Save and continue.
On the Destination page, select a catalog and a schema to load data into. (Optional) If you have USE CATALOG and CREATE SCHEMA privileges on the catalog, you can click Create schema in the drop-down menu to create a new schema.
Click Save and continue.
(Optional) On the Schedules and notifications page, click Create schedule. Set the frequency to refresh the destination tables.
(Optional) Click Add notification to set email notifications for pipeline operation success or failure, then click Save and run pipeline.

Use Declarative Automation Bundles to manage Salesforce pipelines as code. Bundles can contain YAML definitions of jobs and tasks, are managed using the Databricks CLI, and can be shared and run in different target workspaces (such as development, staging, and production). For more information, see What are Declarative Automation Bundles?.

Create a new bundle using the Databricks CLI:
Bash
```
databricks bundle init
```
Add two new resource files to the bundle:
- A pipeline definition file (for example, resources/sfdc_pipeline.yml). See pipeline.ingestion_definition and Examples.
- A job definition file that controls the frequency of data ingestion (for example, resources/sfdc_job.yml).
Deploy the pipeline using the Databricks CLI:
Bash
```
databricks bundle deploy
```

Examples

Use these examples to configure your pipeline.

Ingest formula fields incrementally

Beta

This feature is in Beta.

By default, formula fields are ingested using full snapshots on each pipeline run. However, you can enable incremental ingestion for formula fields by setting the flag pipelines.enableSalesforceFormulaFieldsMVComputation: "true" in the configuration block of your pipeline definition.

The following pipeline definition file enables incremental formula field ingestion:

YAML
resources:
  pipelines:
    pipeline_sfdc:
      name: salesforce_pipeline
      catalog: my_catalog
      schema: my_schema
      configuration:
        pipelines.enableSalesforceFormulaFieldsMVComputation: 'true'
      ingestion_definition:
        connection_name: <salesforce-connection>
        objects:
          - table:
              source_schema: objects
              source_table: Account
              destination_catalog: my_catalog
              destination_schema: my_schema

For more information, see Ingest Salesforce formula fields incrementally.

Ingest two Salesforce objects into separate schemas

The following pipeline definition file ingests two Salesforce objects into separate schemas:

YAML
resources:
  pipelines:
    pipeline_sfdc:
      name: salesforce_pipeline
      catalog: my_catalog_1 # Location of the pipeline event log
      schema: my_schema_1 # Location of the pipeline event log
      ingestion_definition:
        connection_name: <salesforce-connection>
        objects:
          - table:
              source_schema: objects
              source_table: AccountShare
              destination_catalog: my_catalog_1 # Location of this table
              destination_schema: my_schema_1 # Location of this table
          - table:
              source_schema: objects
              source_table: AccountPartner
              destination_catalog: my_catalog_2 # Location of this table
              destination_schema: my_schema_2 # Location of this table

Ingest one Salesforce object three times

The following pipeline definition file ingests a Salesforce object into three different destination tables. You can optionally give ingested tables a new name to differentiate when multiples are ingested into the same destination schema (duplicates aren't supported).

YAML
resources:
  pipelines:
    pipeline_sfdc:
      name: salesforce_pipeline
      catalog: my_catalog_1 # Location of the pipeline event log
      schema: my_schema_1 # Location of the pipeline event log
      ingestion_definition:
        connection_name: <salesforce-connection>
        objects:
          - table:
              source_schema: objects
              source_table: Order
              destination_catalog: my_catalog_1 # Location of first copy
              destination_schema: my_schema_1 # Location of first copy
          - table:
              source_schema: objects
              source_table: Order
              destination_catalog: my_catalog_2 # Location of second copy
              destination_schema: my_schema_2 # Location of second copy
          - table:
              source_schema: objects
              source_table: Order
              destination_catalog: my_catalog_2 # Location of third copy, renamed
              destination_schema: my_schema_2 # Location of third copy, renamed
              destination_table: order_duplicate # Table rename

Bundle job definition file

The following is an example job definition file to use with Declarative Automation Bundles. The job runs every day, exactly one day from the last run.

YAML
resources:
  jobs:
    sfdc_dab_job:
      name: sfdc_dab_job

      trigger:
        periodic:
          interval: 1
          unit: DAYS

      email_notifications:
        on_failure:
          - <email-address>

      tasks:
        - task_key: refresh_pipeline
          pipeline_task:
            pipeline_id: ${resources.pipelines.pipeline_sfdc.id}

Common patterns

For advanced pipeline configurations, see Common patterns for managed ingestion pipelines.

Next steps

Start, schedule, and set alerts on your pipeline. See Common pipeline maintenance tasks.

Requirements​

Create an ingestion pipeline​

Examples​

Ingest formula fields incrementally​

Ingest two Salesforce objects into separate schemas​

Ingest one Salesforce object three times​

Bundle job definition file​

Common patterns​

Next steps​

Additional resources​