DatriseAI-first ETL

Zendesk Support to ClickHouse Integration

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

How Datrise loads Zendesk Support into ClickHouse

Datrise syncs Zendesk Support's tickets, users, organizations, macros, satisfaction ratings, and ticket comments 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

Zendesk Support: Customer support ticketing and agent workflows.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Zendesk Support entities map to ClickHouse

Zendesk Support entityClickHouse objectNotes
ticketszendesk_support_ticketsid PK · custom fields → JSON or Map columns
userszendesk_support_usersid PK · linked to zendesk_support_tickets
organizationszendesk_support_organizationsid PK · linked to zendesk_support_tickets
macroszendesk_support_macrosid PK · linked to zendesk_support_tickets

FAQ

How does Datrise handle Zendesk Support'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 Zendesk Support 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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