DatriseAI-first ETL

Apollo Amazon Redshift

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

How Datrise loads Apollo into Amazon Redshift

Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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

Apollo: Sales intelligence and engagement platform with account-level activity.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How Apollo entities map to Amazon Redshift

Apollo entityAmazon Redshift objectNotes
sales intelligence recordsapollo_sales_intelligence_recordsid PK · custom fields → SUPER columns
account engagementapollo_account_engagementid PK · linked to apollo_sales_intelligence_records
outbound activityapollo_outbound_activityTIMESTAMPTZ events

FAQ

How does Datrise handle Apollo'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 Apollo 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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