DatriseAI-first ETL

Redshift to ClickHouse Integration

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

How Datrise loads Redshift into ClickHouse

Datrise syncs Redshift's tables, rows, schemas, sort keys, distribution keys, and query history 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

Redshift: Columnar cloud data warehouse on AWS.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Redshift entities map to ClickHouse

Redshift entityClickHouse objectNotes
tablesredshift_tablesid PK · custom fields → JSON or Map columns
rowsredshift_rowsid PK · linked to redshift_tables
schemasredshift_schemasid PK · linked to redshift_tables
sort keysredshift_sort_keysid PK · linked to redshift_tables

FAQ

How does Datrise handle Redshift'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 Redshift 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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