DatriseAI-first ETL

Maximizer CRM DuckDB

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

How Datrise loads Maximizer CRM into DuckDB

Datrise syncs Maximizer CRM'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

Maximizer CRM: CRM for SMB teams managing pipeline, contacts, and customer activity.

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

How Maximizer CRM entities map to DuckDB

Maximizer CRM entityDuckDB objectNotes
contactsmaximizer_contactsid PK · custom fields → JSON or STRUCT columns
accountsmaximizer_accountsid PK · linked to maximizer_contacts
dealsmaximizer_dealsid PK · linked to maximizer_contacts
activitiesmaximizer_activitiesTIMESTAMP WITH TIME ZONE events

FAQ

How does Datrise handle Maximizer CRM'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 Maximizer CRM 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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