DatriseAI-first ETL

HubSpot Service Hub DuckDB

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

How Datrise loads HubSpot Service Hub into DuckDB

Datrise syncs HubSpot Service Hub's contacts, accounts, deals, activities, and lifecycle events into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

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

DuckDB: In-process analytics database for fast local OLAP.

How HubSpot Service Hub entities map to DuckDB

HubSpot Service Hub entityDuckDB objectNotes
contactshubspot_service_contactsid PK · custom fields → JSON or STRUCT columns
accountshubspot_service_accountsid PK · linked to hubspot_service_contacts
dealshubspot_service_dealsid PK · linked to hubspot_service_contacts
activitieshubspot_service_activitiesTIMESTAMP WITH TIME ZONE events

FAQ

How does Datrise handle HubSpot Service Hub's custom fields in DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the HubSpot Service Hub to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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