How fresh will the data be?

Typically 5-15 minute lag with cron. Sub-minute with webhooks.

Soft-delete in the warehouse when LinkedIn marks a record deleted. Never hard-delete.

Negligible for most teams — thousands of rows per minute is cheap on modern warehouses.

Fivetran alternative?

Yes — for many teams n8n is a full Fivetran replacement at 1/10th the cost.

Integrations8 min readUpdated 2026-06-29

Reliable, near-real-time sync between LinkedIn and Snowflake, BigQuery or Postgres using n8n.

Getting LinkedIn data into your warehouse is table stakes for modern analytics. n8n gives you the flexibility of Fivetran without the price tag.

Choose full-refresh vs incremental. Incremental is cheaper but needs a stable updated-at timestamp on LinkedIn records.

Run a one-time full-refresh workflow with paging and a Wait node to respect rate limits. Save the max updated-at as your watermark.

Every 5-15 minutes, pull records where updated-at > watermark. Upsert into the warehouse. Advance the watermark last.

Alert if lag exceeds 30 minutes. Alert if row counts drop unexpectedly. Both are early warnings that something upstream broke.

How fresh will the data be?: Typically 5-15 minute lag with cron. Sub-minute with webhooks.
What about deletes?: Soft-delete in the warehouse when LinkedIn marks a record deleted. Never hard-delete.
Warehouse cost?: Negligible for most teams — thousands of rows per minute is cheap on modern warehouses.
Fivetran alternative?: Yes — for many teams n8n is a full Fivetran replacement at 1/10th the cost.