DatriseAI-first ETL

HubSpot Service Hub Amazon Redshift

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

How Datrise loads HubSpot Service Hub into Amazon Redshift

Datrise syncs HubSpot Service Hub's contacts, accounts, deals, activities, and lifecycle events 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

HubSpot Service Hub: Customer service platform with ticket and conversation context.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How HubSpot Service Hub entities map to Amazon Redshift

HubSpot Service Hub entityAmazon Redshift objectNotes
contactshubspot_service_contactsid PK · custom fields → SUPER columns
accountshubspot_service_accountsid PK · linked to hubspot_service_contacts
dealshubspot_service_dealsid PK · linked to hubspot_service_contacts
activitieshubspot_service_activitiesTIMESTAMPTZ events

FAQ

How does Datrise handle HubSpot Service Hub'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 HubSpot Service Hub 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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