DatriseAI-first ETL

Intercom ClickHouse

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

How Datrise loads Intercom into ClickHouse

Datrise syncs Intercom's conversations, customer attributes, inbox events, and support engagement into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

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

ClickHouse: Columnar OLAP engine for fast aggregations.

How Intercom entities map to ClickHouse

Intercom entityClickHouse objectNotes
conversationsintercom_conversationsid PK · custom fields → JSON or Map columns
customer attributesintercom_customer_attributesid PK · linked to intercom_conversations
inbox eventsintercom_inbox_eventsDateTime64 events
support engagementintercom_support_engagementid PK · linked to intercom_conversations

FAQ

How does Datrise handle Intercom's custom fields in ClickHouse?

Flexible values are stored as JSON or Map columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ClickHouse types.

How does the Intercom to ClickHouse sync stay up to date?

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

Related pipelines

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