DatriseAI-first ETL

HubSpot Service Hub to Amazon Redshift Integration

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 tickets, contacts, companies, conversations, knowledge base articles, and feedback submissions 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: Ticketing and knowledge base on the HubSpot CRM record.

Amazon Redshift: AWS petabyte-scale warehouse with Spectrum.

How HubSpot Service Hub entities map to Amazon Redshift

HubSpot Service Hub entityAmazon Redshift objectNotes
ticketshubspot_service_ticketsid PK · custom fields → SUPER columns
contactshubspot_service_contactsid PK · linked to hubspot_service_tickets
companieshubspot_service_companiesid PK · linked to hubspot_service_tickets
conversationshubspot_service_conversationsid PK · linked to hubspot_service_tickets

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

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