DatriseAI-first ETL

Parquet File to Amazon Redshift Integration

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

How Datrise loads Parquet File into Amazon Redshift

Datrise syncs Parquet File's row groups, columns, schemas, partitions, file batches, and column statistics into Amazon Redshift as a typed table per source entity. Flexible or custom fields land in SUPER columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMPTZ.

Sync is incremental: Datrise uses COPY from staged files, then a delete-and-insert merge on stable id, so re-runs update only what changed. A DISTKEY on the join id and a SORTKEY on the load timestamp. Redshift performance hinges on dist/sort keys, so Datrise picks them from your entity ids and sync timestamps rather than defaulting to EVEN distribution.

Ideal for AWS-native warehouses already using the Redshift ecosystem.

Endpoints

Parquet File: Columnar Parquet files from lakes and exports.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Parquet File entities map to Amazon Redshift

Parquet File entityAmazon Redshift objectNotes
row groupsparquet_file_row_groupsid PK · custom fields → SUPER columns
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 Redshift?

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

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

It runs incrementally — Datrise uses COPY from staged files, then a delete-and-insert merge on stable id.

Related pipelines

Early access

Connect Parquet File to Amazon Redshift the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.