`aidp-salesforce` — Salesforce via AIDP `aidataplatform`

aidp-salesforce — Salesforce via AIDP aidataplatform

When to use

• User wants to ingest Salesforce data (Account, Opportunity, Lead, Contact, custom sObjects) into a Spark DataFrame from an AIDP notebook.
• User mentions: "Salesforce", "SFDC", "Sales Cloud", "Service Cloud", "sObject", "SOQL", a Salesforce object name (Account, Opportunity, etc.).

When NOT to use

• For arbitrary REST APIs with a custom manifest → aidp-rest-generic.
• For Oracle CX/CRM → aidp-siebel (Siebel) or Fusion CX via aidp-fusion-rest.

Prerequisites in the AIDP notebook

1. Helpers on sys.path (run aidp-connectors-bootstrap first).
2. Env vars / OCI Vault secrets:
   • SFDC_HOST (Salesforce login host, e.g. login.salesforce.com or <my-domain>.my.salesforce.com)
   • SFDC_PORT (typically 443)
   • SFDC_DATABASE_NAME (org name / database identifier; for most tenants this is just the org name)
   • SFDC_USER (Salesforce username — typically <email>)
   • SFDC_PASSWORD (password concatenated with security token: <password><security-token>)
   • SFDC_SCHEMA (typically SFORCE for the connector)
   • SFDC_TABLE (sObject API name, e.g. Account, Opportunity, Custom_Object__c)

Read

import os
from oracle_ai_data_platform_connectors.aidataplatform import (
    AIDP_FORMAT, aidataplatform_options,
)

opts = aidataplatform_options(
    type="SFORCE",
    host=os.environ["SFDC_HOST"],
    port=int(os.environ.get("SFDC_PORT", "443")),
    database_name=os.environ["SFDC_DATABASE_NAME"],
    user=os.environ["SFDC_USER"],
    password=os.environ["SFDC_PASSWORD"],
    schema=os.environ.get("SFDC_SCHEMA", "SFORCE"),
    table=os.environ["SFDC_TABLE"],   # e.g. "Account", "Opportunity"
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()
df.show(10)

Pushdown SQL

Use pushdown.sql to push a SOQL-like query at the source — useful for filtering on indexed fields, joins via relationship paths, or LIMIT semantics.

opts = aidataplatform_options(
    type="SFORCE",
    host=os.environ["SFDC_HOST"],
    port=int(os.environ.get("SFDC_PORT", "443")),
    database_name=os.environ["SFDC_DATABASE_NAME"],
    user=os.environ["SFDC_USER"],
    password=os.environ["SFDC_PASSWORD"],
    extra={
        "pushdown.sql": (
            "SELECT Id, Name, AnnualRevenue, BillingCountry "
            "FROM Account "
            "WHERE AnnualRevenue > 1000000 AND BillingCountry = 'United States'"
        ),
    },
)
df = spark.read.format(AIDP_FORMAT).options(**opts).load()
df.show(10)

Gotchas

• type is SFORCE, not SALESFORCE. Easy to get wrong if you're following the human-readable name. The connector type literally says SFORCE.
• Password requires the security token appended. Salesforce username/password auth needs <password><security-token> concatenated as a single string. The security token is reset each time the user changes their password — emailed to the user.
• API limits. Salesforce enforces per-org daily API call quotas. Bulk reads count against the quota. Use pushdown.sql with selective filters and field projection to minimize calls.
• Connector is read-only. Salesforce writes (create/update sObjects) need to go through the Salesforce REST/Bulk API or the Composite API directly.
• Custom objects end in __c (e.g. Project__c). Custom fields end in __c too — always include the trailing __c in pushdown.sql.
• Field-level security. The connector user inherits Salesforce profile + permission sets. Fields hidden by FLS won't appear in the result.

References

• Helper: scripts/oracle_ai_data_platform_connectors/aidataplatform.py
• Official sample: oracle-samples/oracle-aidp-samples → data-engineering/ingestion/Read_Only_Ingestion_Connectors/Salesforce.ipynb