DataKnits | Load Salesforce Data into BigQuery

Why Sync Salesforce Into BigQuery

Salesforce holds the CRM system of record, but blending it with product usage, marketing, or finance data for reporting requires a warehouse. Loading Salesforce objects into BigQuery lets analysts join CRM data with everything else in one place using standard SQL and BI tools.

How DataKnits Builds the Pipeline

✓ Authenticate to Salesforce via OAuth — no static API keys to manage
✓ Pick standard or custom Salesforce objects as the source
✓ Map fields to a BigQuery dataset and table schema visually
✓ Rate limits and pagination are handled by the compiled connector
✓ Schedule recurring syncs to keep BigQuery current with Salesforce

Compiled Output (Example)

df_sf = spark.read.format("salesforce")
  .option("object", "Opportunity")
  .load()

df_sf.write.format("bigquery")
  .option("table", "crm.opportunities")
  .mode("append").save()

Frequently Asked Questions

How do I load Salesforce data into BigQuery?

In DataKnits, add Salesforce as a source node and authenticate via OAuth, choose the objects you need (Accounts, Opportunities, Leads, custom objects), map fields to a BigQuery destination schema visually, and DataKnits compiles the pipeline into native code that handles Salesforce's API rate limits and pagination.

Does DataKnits handle Salesforce API rate limits automatically?

Yes, rate-limiting and pagination handling for the Salesforce REST API are built into the connector, so pipelines don't need custom retry logic written by hand.

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