ClickHouse to Chartio Integration
AI-first ETL from ClickHouse into Chartio. Governed entities, incremental sync, typed landing tables.
How Datrise loads ClickHouse into Chartio
Datrise syncs ClickHouse's tables, rows, partitions, materialized views, table schemas, and merge parts into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.
Ideal for drag-and-drop charting over a database.
Endpoints
ClickHouse: Column-oriented database for real-time analytics.
Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.
How ClickHouse entities map to Chartio
| ClickHouse entity | Chartio object | Notes |
|---|---|---|
| tables | clickhouse_tables | id PK · custom fields → flattened columns for visual SQL |
| rows | clickhouse_rows | id PK · linked to clickhouse_tables |
| partitions | clickhouse_partitions | id PK · linked to clickhouse_tables |
| materialized views | clickhouse_materialized_views | id PK · linked to clickhouse_tables |
FAQ
How does Datrise handle ClickHouse's custom fields in Chartio?
Flexible values are stored as flattened columns for visual SQL, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Chartio types.
How does the ClickHouse to Chartio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for ClickHouse
Early access
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