Apollo → ClickHouse
AI-first ETL from Apollo into ClickHouse. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into ClickHouse
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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
Apollo: Sales intelligence and engagement platform with account-level activity.
ClickHouse: Columnar OLAP engine for fast aggregations.
How Apollo entities map to ClickHouse
| Apollo entity | ClickHouse object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → JSON or Map columns |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | DateTime64 events |
FAQ
How does Datrise handle Apollo'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 Apollo 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
More destinations for Apollo
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