CallRail to DuckDB Integration

How Datrise loads CallRail into DuckDB

Datrise syncs CallRail's calls, trackers, form submissions, sources, call outcomes, and recordings 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

CallRail: Call tracking and marketing attribution.

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

How CallRail entities map to DuckDB

CallRail entity	DuckDB object	Notes
calls	`callrail_calls`	id PK · custom fields → JSON or STRUCT columns
trackers	`callrail_trackers`	id PK · linked to callrail_calls
form submissions	`callrail_form_submissions`	id PK · linked to callrail_calls
sources	`callrail_sources`	id PK · linked to callrail_calls

FAQ

How does Datrise handle CallRail'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 CallRail 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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