DatriseAI-first ETL

Intercom Google BigQuery

AI-first ETL from Intercom into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Intercom into Google BigQuery

Datrise syncs Intercom's conversations, customer attributes, inbox events, and support engagement 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

Intercom: Customer messaging platform with CRM-style account and conversation context.

Google BigQuery: Serverless analytics warehouse on GCP.

How Intercom entities map to Google BigQuery

Intercom entityGoogle BigQuery objectNotes
conversationsintercom_conversationsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
customer attributesintercom_customer_attributesid PK · linked to intercom_conversations
inbox eventsintercom_inbox_eventsTIMESTAMP events
support engagementintercom_support_engagementid PK · linked to intercom_conversations

FAQ

How does Datrise handle Intercom'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 Intercom 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

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