DatriseAI-first ETL

ClickHouse to Yellowfin Integration

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

How Datrise loads ClickHouse into Yellowfin

Datrise syncs ClickHouse's tables, rows, partitions, materialized views, table schemas, and merge parts into Yellowfin as warehouse tables Yellowfin builds views on. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Yellowfin views reference columns by name, so Datrise lands stable, well-typed columns to keep reports valid.

Ideal for dashboards with automated data storytelling.

Endpoints

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

Yellowfin: BI suite with dashboards, automated insights, and data storytelling.

How ClickHouse entities map to Yellowfin

ClickHouse entityYellowfin objectNotes
tablesclickhouse_tablesid PK · custom fields → flattened 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 Yellowfin?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

Early access

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