DatriseAI-first ETL

ClickHouse to PostgreSQL Integration

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

How Datrise loads ClickHouse into PostgreSQL

Datrise syncs ClickHouse's tables, rows, partitions, materialized views, table schemas, and merge parts into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

ClickHouse: Column-oriented database for real-time analytics.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How ClickHouse entities map to PostgreSQL

ClickHouse entityPostgreSQL objectNotes
tablesclickhouse_tablesid PK · custom fields → jsonb columns
rowsclickhouse_rowsid PK · linked to clickhouse_tables
partitionsclickhouse_partitionsid PK · linked to clickhouse_tables
materialized viewsclickhouse_materialized_viewsid PK · linked to clickhouse_tables

FAQ

How does Datrise handle ClickHouse's custom fields in PostgreSQL?

Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.

How does the ClickHouse to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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