DatriseAI-first ETL

Freshcaller to ClickHouse Integration

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

How Datrise loads Freshcaller into ClickHouse

Datrise syncs Freshcaller's calls, agents, queues, phone numbers, call metrics, and recordings 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

Freshcaller: Cloud call center for support and sales.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Freshcaller entities map to ClickHouse

Freshcaller entityClickHouse objectNotes
callsfreshcaller_callsid PK · custom fields → JSON or Map columns
agentsfreshcaller_agentsid PK · linked to freshcaller_calls
queuesfreshcaller_queuesid PK · linked to freshcaller_calls
phone numbersfreshcaller_phone_numbersid PK · linked to freshcaller_calls

FAQ

How does Datrise handle Freshcaller'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 Freshcaller 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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