DatriseAI-first ETL

Google Analytics Amazon Redshift

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

How Datrise loads Google Analytics into Amazon Redshift

Datrise syncs Google Analytics's sessions, events, channels, conversions, and behavior cohorts 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

Google Analytics: Web and product analytics for behavior and traffic insights.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Google Analytics entities map to Amazon Redshift

Google Analytics entityAmazon Redshift objectNotes
sessionsgoogle_analytics_sessionsid PK · custom fields → SUPER columns
eventsgoogle_analytics_eventsTIMESTAMPTZ events
channelsgoogle_analytics_channelsid PK · linked to google_analytics_sessions
conversionsgoogle_analytics_conversionsid PK · linked to google_analytics_sessions

FAQ

How does Datrise handle Google Analytics'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 Google Analytics 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

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