Drift to Google BigQuery Integration

How Datrise loads Drift into Google BigQuery

Datrise syncs Drift's conversations, contacts, playbooks, meetings, messages, and accounts into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

Drift: Conversational marketing and sales chat.

Google BigQuery: Serverless analytics warehouse on GCP.

How Drift entities map to Google BigQuery

Drift entity	Google BigQuery object	Notes
conversations	`drift_conversations`	id PK · custom fields → JSON or nested/repeated (STRUCT) columns
contacts	`drift_contacts`	id PK · linked to drift_conversations
playbooks	`drift_playbooks`	id PK · linked to drift_conversations
meetings	`drift_meetings`	id PK · linked to drift_conversations

FAQ

How does Datrise handle Drift's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Drift to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

Connect Drift to Google BigQuery the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.