DatriseAI-first ETL

Real Geeks to Snowflake Integration

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

How Datrise loads Real Geeks into Snowflake

Datrise syncs Real Geeks's leads, contacts, listings, saved searches, property views, and agent follow-ups 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

Real Geeks: Real estate lead generation sites with an integrated CRM.

Snowflake: Cloud data warehouse with separated compute and storage.

How Real Geeks entities map to Snowflake

Real Geeks entitySnowflake objectNotes
leadsreal_geeks_leadsid PK · custom fields → VARIANT columns
contactsreal_geeks_contactsid PK · linked to real_geeks_leads
listingsreal_geeks_listingsid PK · linked to real_geeks_leads
saved searchesreal_geeks_saved_searchesid PK · linked to real_geeks_leads

FAQ

How does Datrise handle Real Geeks'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 Real Geeks 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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