DatriseAI-first ETL

Parquet File to Chartio Integration

AI-first ETL from Parquet File into Chartio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Parquet File into Chartio

Datrise syncs Parquet File's row groups, columns, schemas, partitions, file batches, and column statistics 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

Parquet File: Columnar Parquet files from lakes and exports.

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Parquet File entities map to Chartio

Parquet File entityChartio objectNotes
row groupsparquet_file_row_groupsid PK · custom fields → flattened columns for visual SQL
columnsparquet_file_columnsid PK · linked to parquet_file_row_groups
schemasparquet_file_schemasid PK · linked to parquet_file_row_groups
partitionsparquet_file_partitionsid PK · linked to parquet_file_row_groups

FAQ

How does Datrise handle Parquet File'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 Parquet File to Chartio sync stay up to date?

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

Related pipelines

Early access

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