Marketers increasingly rely on BigQuery as a centralized analytics hub where cleaned, production-ready data powers segmentation, modeling, and reporting. Fivetran offers a managed EL solution that automates extraction, transformation, and loading so your teams can focus on insights rather than plumbing.
This guide explains how to sync marketing data with BigQuery using the Fivetran Free plan, covering setup, limitations, and best practices for marketers working with Google Ads, Meta, Google Analytics, and similar sources.
| Plan | Monthly Rows | Sources Included | Transformation |
|---|---|---|---|
| Free | 75,000 | 70+ core connectors | No built-in dbt or data quality tools |
| Starter | 150,000 | All core connectors | Basic normalization |
| Growth | 300,000 | All connectors plus Slack & more | SQL-based transformations |
| Enterprise | Unlimited | All features and SSO | Full ELT with orchestration |
Preparing for BigQuery Sync
Setting Up a BigQuery Project
Before you connect marketing sources, create a dedicated BigQuery project with clear naming to isolate marketing datasets from analytics or product workloads. Enable billing, even though Fivetran Free uses minimal BigQuery slots, and configure dataset locations to match your regional compliance needs.
Gathering Marketing Source Credentials
Collect credentials for each marketing system you plan to sync, such as Google Ads, Meta Ads, Google Analytics, LinkedIn, and TikTok Ads. Ensure the service accounts or tokens have read-only permissions limited to the reports and fields required for reporting so Fivetran can authenticate securely.
Syncing Marketing Data with Fivetran Free
Adding Connectors Step by Step
Log in to Fivetran, choose Add Connector, and pick your marketing source. Enter the project or org credentials, select the BigQuery destination dataset, and map the sync schedule to off-peak hours to minimize slot contention. With the Free plan, you can add up to three connectors and rely on predefined schemas that map fields like campaign, ad_group, and metrics directly into BigQuery tables.
Monitoring Initial Loads and Incremental Syncs
After saving a connector, trigger a historical backfill and then verify that raw tables appear in your datasets with correct column types. Watch the sync logs for errors such as rate limits or schema changes, and use Fivetran’s pause and retry options to stabilize throughput during the first weeks.
Managing Costs and Performance on Free
Optimizing Row Volume and Sync Frequency
The Fivetran Free plan includes 75,000 rows per month across all connectors, so prioritize critical marketing sources and consider daily or hourly syncs only for high-value campaigns. Use column selection to exclude wide fields or unnecessary dimensions, and leverage BigQuery partitioned tables to control storage costs when large date-partitioned tables accumulate over time.
Schema Evolution and Field Mapping
Marketing platforms occasionally add new metrics or rename fields, which can cause sync warnings. Review schema change notifications in Fivetran, map new fields to existing BigQuery columns where possible, and snapshot important table versions before major updates so you can preserve historical definitions for trend analysis.
Ensuring Data Quality and Governance
Validating Marketing Reports
Build lightweight checks in BigQuery such as row counts, null rate thresholds on key dimensions like campaign_id, and sanity checks on cost metrics like cost_per_conversion. Use scheduled queries or simple alerts to surface drops in data freshness or unexpected spikes that could indicate sync issues before stakeholders notice.
Access Control and Sharing
Leverage BigQuery’s role-based access control to give analysts read access to raw marketing tables while restricting edit rights to curated views. Document naming conventions, source-of-truth owners, and retention policies so marketing, finance, and data teams understand who maintains each dataset and how long records are retained.
Next Steps for Marketing Data Teams
- Create a dedicated BigQuery project and dataset for marketing reporting.
- Start with up to three core marketing connectors and validate row counts and field mappings.
- Implement partitioned tables and scheduled queries to control costs on large datasets.
- Set up schema change alerts and access controls to maintain data quality and governance.
- Iterate on sync frequency and column selection to stay within the Free plan limits while meeting reporting cadence needs.
FAQ
Reader questions
How many marketing connectors can I add with Fivetran Free?
You can add up to three connectors on the Free plan, which is often enough to cover Google Ads, Meta Ads, and Google Analytics for most small to mid-sized teams.
Will my BigQuery slot usage increase significantly after enabling marketing syncs?
Fivetran loads data efficiently, and typical marketing datasets consume modest slots; monitor your quota dashboard and consider clustering by date or campaign_id to optimize performance and cost.
What happens if I exceed the 75,000 monthly row limit?
Syncs will pause until the next billing cycle, so prioritize essential sources and consider adjusting sync frequency or upgrading if consistent row volume regularly exceeds the limit.
Can I transform marketing data in BigQuery instead of using Fivetran mappings?
Yes, you can keep raw tables as ingested by Fivetran and build transformation layers in BigQuery using SQL or templated notebooks, which is a common pattern for marketing teams that need custom calculations and historical consistency.