Vitally to ClickHouse Integration
AI-first ETL from Vitally into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vitally into ClickHouse
Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage 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
Vitally: Customer success health and account analytics.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Vitally entities map to ClickHouse
| Vitally entity | ClickHouse object | Notes |
|---|---|---|
| accounts | vitally_accounts | id PK · custom fields → JSON or Map columns |
| users | vitally_users | id PK · linked to vitally_accounts |
| health scores | vitally_health_scores | id PK · linked to vitally_accounts |
| tasks | vitally_tasks | id PK · linked to vitally_accounts |
FAQ
How does Datrise handle Vitally'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 Vitally 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.
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