DatriseAI-first ETL

Efficy CRM Snowflake

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

How Datrise loads Efficy CRM into Snowflake

Datrise syncs Efficy CRM's contacts, accounts, deals, activities, and lifecycle 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

Efficy CRM: European CRM for SMB and mid-market sales teams.

Snowflake: Cloud data warehouse with separated compute and storage.

How Efficy CRM entities map to Snowflake

Efficy CRM entitySnowflake objectNotes
contactsefficy_contactsid PK · custom fields → VARIANT columns
accountsefficy_accountsid PK · linked to efficy_contacts
dealsefficy_dealsid PK · linked to efficy_contacts
activitiesefficy_activitiesTIMESTAMP_TZ events

FAQ

How does Datrise handle Efficy 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 Efficy 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

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