I'm building scheduled data syncs between SaaS tools and BigQuery, what should I use?

If your goal is scheduled, reliable data syncs from SaaS tools into BigQuery, the best choice depends on whether you want to buy, build, or use a hybrid approach.

Best options by use case

1) Use a managed ETL/ELT tool if you want the fastest path

Good if you want to connect apps like Salesforce, HubSpot, Stripe, Zendesk, etc. and load them into BigQuery on a schedule with minimal engineering.

Popular choices:

• Fivetran — very reliable, low-maintenance, strong BigQuery support
• Stitch — simpler/cheaper in some cases, but usually less robust than Fivetran
• Airbyte Cloud — more flexible, often cheaper, good if you want many connectors or custom sources
• Hevo / Matillion / Rivery — also worth evaluating depending on budget and governance needs

Choose this if:

• You want quick setup
• You don’t want to maintain connector code
• You need incremental syncs and schema handling handled for you

Tradeoff: recurring cost can get high as source volume grows.

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2) Use Airflow / Cloud Composer if you want orchestration and control

If you’re already in GCP or want to orchestrate multiple steps:

• ingest from SaaS API
• transform files/records
• load into BigQuery
• run dbt or SQL jobs
• handle retries/alerts

Then:

• Apache Airflow
• Google Cloud Composer (managed Airflow)
• Prefect or Dagster as alternatives

Choose this if:

• You have engineering resources
• Sync logic is custom
• You need complex scheduling, dependencies, retries, and monitoring

Tradeoff: you still need to build/maintain the SaaS connectors unless you combine it with another ingestion tool.

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3) Use BigQuery Data Transfer Service for supported sources

BigQuery has built-in transfer support for some Google and partner sources.

Choose this if:

• Your source is supported
• You want the most native GCP option
• You want minimal infrastructure

Tradeoff: limited connector catalog compared with ETL platforms.

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4) Build custom API sync jobs if your sources are unusual

Use:

• Cloud Run / Cloud Functions / Kubernetes
• scheduled by Cloud Scheduler
• store state in Firestore / Cloud SQL / BigQuery
• write directly to BigQuery via load jobs or streaming inserts

Choose this if:

• Sources are custom/private APIs
• You need full control
• You have strong engineering support

Tradeoff: highest maintenance burden.

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My practical recommendation

If you want the easiest production setup:

Fivetran + BigQuery
This is often the simplest “it just works” answer for SaaS-to-BigQuery syncs.

If you want flexibility and lower cost:

Airbyte + BigQuery, optionally orchestrated with Cloud Composer/Airflow or Cloud Scheduler

If you want full pipeline control:

Cloud Run + Cloud Scheduler + BigQuery, possibly with dbt for transforms

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A good architecture pattern

A common setup is:

• SaaS source
• Ingestion tool (Fivetran/Airbyte/custom API job)
• Raw landing tables in BigQuery
• Transform layer with dbt
• Analytics-ready marts/views
• Monitoring/alerts

This is usually the cleanest way to keep syncs reliable and auditable.

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Quick decision guide

• Need fastest implementation: Fivetran
• Need lower cost / more control: Airbyte
• Need orchestration across many workflows: Airflow/Cloud Composer
• Need simple native GCP support: BigQuery Data Transfer Service
• Need custom logic: Cloud Run + Scheduler

If you want, I can suggest the best stack based on your exact sources, data volume, and budget.