Vincle → ClickHouse
AI-first ETL from Vincle into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vincle into ClickHouse
Datrise syncs Vincle's contacts, accounts, deals, activities, and lifecycle events 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
Vincle: European CRM for SMB and mid-market sales teams.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Vincle entities map to ClickHouse
| Vincle entity | ClickHouse object | Notes |
|---|---|---|
| contacts | vincle_contacts | id PK · custom fields → JSON or Map columns |
| accounts | vincle_accounts | id PK · linked to vincle_contacts |
| deals | vincle_deals | id PK · linked to vincle_contacts |
| activities | vincle_activities | DateTime64 events |
FAQ
How does Datrise handle Vincle'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 Vincle 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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