DatriseAI-first ETL

Amazon Amazon S3 ClickHouse

AI-first ETL from Amazon Amazon S3 into ClickHouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon Amazon S3 into ClickHouse

Datrise syncs Amazon Amazon S3's records, events, and configuration objects 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

Amazon Amazon S3: SaaS or API data source for analytics and warehouse sync.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Amazon Amazon S3 entities map to ClickHouse

Amazon Amazon S3 entityClickHouse objectNotes
recordsamazon_s3_recordsid PK · custom fields → JSON or Map columns
eventsamazon_s3_eventsDateTime64 events
configuration objectsamazon_s3_configuration_objectsid PK · linked to amazon_s3_records

FAQ

How does Datrise handle Amazon Amazon S3'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 Amazon Amazon S3 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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