Fivetran Best Practices for Reliable Pipelines
Keep your Fivetran–Snowflake pipelines predictable and easy to operate.
Use a dedicated destination and schema strategy
Give Fivetran its own Snowflake database (or at least a dedicated schema) so raw data is isolated from your transformed data. Use a naming convention for connector schemas (e.g. fivetran_<connector_name>) so downstream dbt sources are easy to find. Separate destinations per environment (dev, prod) so you can test new connectors or schema changes without affecting production.
Monitor syncs and handle schema changes
Check Fivetran’s sync history and logs regularly. Failed syncs are flagged; fix permission or schema issues at the source or in the connector config. Enable schema change alerts so you’re notified when Fivetran detects new, removed, or changed columns. Decide in advance whether to auto-accept certain changes or review them so downstream dbt models don’t break unexpectedly. Document which tables are critical and set up simple monitoring (e.g. row counts, freshness) in dbt or your BI tool.
Sync frequency and cost
Choose sync frequency based on how fresh the data needs to be and how much you’re willing to spend (Fivetran bills on monthly active rows). High-volume connectors can be synced less often if batch reporting is acceptable; key sources for operational dashboards may need hourly or more frequent syncs. Use Fivetran’s column blocking and optional historical sync limits to avoid syncing unnecessary data and keep pipeline cost and load time under control.
Need more reliable Snowflake pipelines?
Kundul helps teams improve Fivetran sync reliability, downstream dbt transformations, and the operating model behind production Snowflake data engineering.
Book a call