DatriseAI-first ETL

Day.ai Snowflake

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

How Datrise loads Day.ai into Snowflake

Datrise syncs Day.ai'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

Day.ai: AI-native CRM for relationship data, enrichment, and workflow automation.

Snowflake: Cloud data warehouse with separated compute and storage.

How Day.ai entities map to Snowflake

Day.ai entitySnowflake objectNotes
contactsday_ai_contactsid PK · custom fields → VARIANT columns
accountsday_ai_accountsid PK · linked to day_ai_contacts
dealsday_ai_dealsid PK · linked to day_ai_contacts
activitiesday_ai_activitiesTIMESTAMP_TZ events

FAQ

How does Datrise handle Day.ai'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 Day.ai 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

Early access

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