DatriseAI-first ETL

Wise Agent to Snowflake Integration

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

How Datrise loads Wise Agent into Snowflake

Datrise syncs Wise Agent's contacts, transactions, listings, commissions, referrals, and checklists 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

Wise Agent: Real estate CRM for transactions, commissions, and referrals.

Snowflake: Cloud data warehouse with separated compute and storage.

How Wise Agent entities map to Snowflake

Wise Agent entitySnowflake objectNotes
contactswise_agent_contactsid PK · custom fields → VARIANT columns
transactionswise_agent_transactionsid PK · linked to wise_agent_contacts
listingswise_agent_listingsid PK · linked to wise_agent_contacts
commissionswise_agent_commissionsid PK · linked to wise_agent_contacts

FAQ

How does Datrise handle Wise Agent'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 Wise Agent 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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