DatriseAI-first ETL

HubSpot CRM Snowflake

AI-first ETL from HubSpot CRM into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads HubSpot CRM into Snowflake

Datrise syncs HubSpot CRM's contacts, companies, deals, lifecycle stages, and engagement events into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

HubSpot CRM: Inbound CRM with marketing, sales, and service hubs.

Snowflake: Cloud data warehouse with separated compute and storage.

How HubSpot CRM entities map to Snowflake

HubSpot CRM entitySnowflake objectNotes
contactshubspot_contactsid PK · custom fields → VARIANT columns
companieshubspot_companiesid PK · linked to hubspot_contacts
dealshubspot_dealsid PK · linked to hubspot_contacts
lifecycle stageshubspot_lifecycle_stagesid PK · linked to hubspot_contacts

FAQ

How does Datrise handle HubSpot CRM's custom fields in Snowflake?

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

How does the HubSpot CRM to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

Early access

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