Freshdesk → ClickHouse
AI-first ETL from Freshdesk into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Freshdesk into ClickHouse
Datrise syncs Freshdesk's tickets, contacts, agents, SLA events, and satisfaction scores 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
Freshdesk: Customer support helpdesk with tickets, SLAs, and agent workflows.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Freshdesk entities map to ClickHouse
| Freshdesk entity | ClickHouse object | Notes |
|---|---|---|
| tickets | freshdesk_tickets | id PK · custom fields → JSON or Map columns |
| contacts | freshdesk_contacts | id PK · linked to freshdesk_tickets |
| agents | freshdesk_agents | id PK · linked to freshdesk_tickets |
| SLA events | freshdesk_sla_events | DateTime64 events |
FAQ
How does Datrise handle Freshdesk'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 Freshdesk 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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