Vitally to DuckDB Integration

How Datrise loads Vitally into DuckDB

Datrise syncs Vitally's accounts, users, health scores, tasks, playbooks, and product usage 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

Vitally: Customer success health and account analytics.

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

How Vitally entities map to DuckDB

Vitally entity	DuckDB object	Notes
accounts	`vitally_accounts`	id PK · custom fields → JSON or STRUCT columns
users	`vitally_users`	id PK · linked to vitally_accounts
health scores	`vitally_health_scores`	id PK · linked to vitally_accounts
tasks	`vitally_tasks`	id PK · linked to vitally_accounts

FAQ

How does Datrise handle Vitally'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 Vitally 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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