DatriseAI-first ETL

Parquet File to Amazon QuickSight Integration

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

How Datrise loads Parquet File into Amazon QuickSight

Datrise syncs Parquet File's row groups, columns, schemas, partitions, file batches, and column statistics into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.

Ideal for AWS-native dashboards with pay-per-session pricing.

Endpoints

Parquet File: Columnar Parquet files from lakes and exports.

Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.

How Parquet File entities map to Amazon QuickSight

Parquet File entityAmazon QuickSight objectNotes
row groupsparquet_file_row_groupsid PK · custom fields → flattened columns for analyses
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 Amazon QuickSight?

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

How does the Parquet File to Amazon QuickSight sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.

Related pipelines

Early access

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