DatriseAI-first ETL

Parquet File Amazon Redshift

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 records, events, and configuration objects 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: SaaS or API data source for analytics and warehouse sync.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Parquet File entities map to Amazon Redshift

Parquet File entityAmazon Redshift objectNotes
recordsparquet_file_recordsid PK · custom fields → SUPER columns
eventsparquet_file_eventsTIMESTAMPTZ events
configuration objectsparquet_file_configuration_objectsid PK · linked to parquet_file_records

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

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