DatriseAI-first ETL

Amazon RDS to ClickHouse Integration

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

How Datrise loads Amazon RDS into ClickHouse

Datrise syncs Amazon RDS's tables, rows, schemas, indexes, primary keys, and change streams 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 RDS: Managed relational database service on AWS.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Amazon RDS entities map to ClickHouse

Amazon RDS entityClickHouse objectNotes
tablesamazon_rds_tablesid PK · custom fields → JSON or Map columns
rowsamazon_rds_rowsid PK · linked to amazon_rds_tables
schemasamazon_rds_schemasid PK · linked to amazon_rds_tables
indexesamazon_rds_indexesid PK · linked to amazon_rds_tables

FAQ

How does Datrise handle Amazon RDS'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 RDS 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

Early access

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