Salesforce to BigQuery integration for analytics-ready CRM data

Extract through the Salesforce API, then load into BigQuery as partitioned tables. Use the Bulk API for the initial backfill of large objects and the REST or Query API with a SystemModstamp watermark for incremental runs. Land each object in its own table, partition on a date column, and merge on the Salesforce record ID so re-runs update rather than duplicate.

Start with Account, Contact, Lead, Opportunity, OpportunityLineItem, User and the history objects you report on. Adding every custom object on day one is the usual mistake: it multiplies API calls, slows the backfill, and fills the warehouse with tables nobody queries. Add objects as reports demand them.

They set your realistic sync frequency. Salesforce enforces a daily API request allocation per org, and a naive per object full refresh burns through it fast. Incremental reads on SystemModstamp cut request volume by orders of magnitude, and the Bulk API moves large volumes in far fewer calls than the REST API.

Query the deleted records endpoint or read the recycle bin, because a SystemModstamp watermark cannot see a row that no longer exists. Without that step, deleted opportunities stay in BigQuery forever and every pipeline report runs slightly high. Some teams also run a periodic ID reconciliation to catch hard deletes.

The Salesforce to BigQuery integration from Adapters loads accounts, contacts, opportunities, and activity from Salesforce into BigQuery tables on an incremental schedule, so your warehouse stays analytics-ready without draining Salesforce API limits. Field mapping takes about a minute and no code is required.

Yes. This Salesforce to BigQuery connector ships prebuilt: the field mapping is wired the moment you pick the pair, transforms are included, and the first sync can run within minutes. No code or engineering sprint required.

Pricing is flat and monthly: Starter at $49, Growth at $149, Scale at $399. Every plan includes this pair, visual field mapping, and per-record logs. There are no per-task or per-row fees, so the bill stays the same as volume grows.

Hourly on Starter, every 5 minutes on Growth, and down to every minute on Scale. Failed records retry automatically with backoff, and alerting plus a full per-record log come standard on every run.

No. Fields are auto-mapped the moment you pick the pair, and you can rewire any mapping visually before the first sync. Incremental loads use the Salesforce SystemModstamp watermark so each run only moves changed rows into BigQuery; Salesforce field types cast to BigQuery column types, currency stays NUMERIC, timestamps land as TIMESTAMP in UTC, and tables partition by load date so dbt models and Looker Studio read a clean, deduplicated table without scanning the whole history.