DatriseAI-first ETL

Zendesk Chat ClickHouse

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

How Datrise loads Zendesk Chat into ClickHouse

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times 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 Chat: Live chat conversations and agent performance.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Zendesk Chat entities map to ClickHouse

Zendesk Chat entityClickHouse objectNotes
chatszendesk_chat_chatsid PK · custom fields → JSON or Map columns
agentszendesk_chat_agentsid PK · linked to zendesk_chat_chats
visitorszendesk_chat_visitorsid PK · linked to zendesk_chat_chats
departmentszendesk_chat_departmentsid PK · linked to zendesk_chat_chats

FAQ

How does Datrise handle Zendesk Chat'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 Chat 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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