Sync from a warehouse

Use Segment's Reverse ETL to sync data from BigQuery, Snowflake, or Redshift into Clarify on an ongoing basis. This is the recommended approach for teams that use a warehouse as their source of truth.

How it works

Your warehouse contains the canonical customer data
Segment queries your warehouse on a schedule (hourly, daily)
Changed records are pushed to Clarify via the API
Clarify matches on email/domain to update existing records or create new ones

Setup

1. Connect your warehouse to Segment

In Segment, add your warehouse as a Source (Reverse ETL):

BigQuery, Snowflake, Redshift, or Databricks
Configure a service account with read access to your tables

2. Add Clarify as a destination

In Segment, add Clarify as a Reverse ETL destination:

Enter your workspace slug and API key
Map your warehouse columns to Clarify fields

3. Define your sync model

Write a SQL query that selects the records you want to sync:

SELECT
  email,
  first_name,
  last_name,
  company_name,
  title,
  lifecycle_stage
FROM analytics.contacts
WHERE updated_at > CURRENT_TIMESTAMP - INTERVAL '24 hours'

4. Map fields

Warehouse column	Clarify field	Notes
`email`	`email_addresses`	Primary match key
`first_name`	`first_name`
`last_name`	`last_name`
`company_domain`	`domains` (on company)	Used for company matching
`lifecycle_stage`	Custom field	Create the field first

5. Set a schedule

Hourly for fast-moving data (active pipeline)
Daily for reference data (company metadata, enrichment)

Matching behavior

Clarify matches incoming records by:

People: email address
Companies: domain

If a match is found, the record is updated. If not, a new record is created.

Tips

Start with a daily sync and move to hourly once you've validated the mapping
Use Segment's Debugger to inspect payloads before they hit Clarify
Create custom fields in Clarify before mapping warehouse columns to them

Sync from a warehouse

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