DatriseAI-first ETL

Salesforce Health Cloud DuckDB

AI-first ETL from Salesforce Health Cloud into DuckDB. Governed entities, incremental sync, typed landing tables.

How Datrise loads Salesforce Health Cloud into DuckDB

Datrise syncs Salesforce Health Cloud'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

Salesforce Health Cloud: Healthcare CRM for accounts, compliance, and field engagement.

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

How Salesforce Health Cloud entities map to DuckDB

Salesforce Health Cloud entityDuckDB objectNotes
contactssalesforce_health_cloud_contactsid PK · custom fields → JSON or STRUCT columns
accountssalesforce_health_cloud_accountsid PK · linked to salesforce_health_cloud_contacts
dealssalesforce_health_cloud_dealsid PK · linked to salesforce_health_cloud_contacts
activitiessalesforce_health_cloud_activitiesTIMESTAMP WITH TIME ZONE events

FAQ

How does Datrise handle Salesforce Health Cloud'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 Salesforce Health Cloud 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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