DatriseAI-first ETL

Delighted Snowflake

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

How Datrise loads Delighted into Snowflake

Datrise syncs Delighted's surveys, responses, scores, and follow-up workflows 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

Delighted: NPS and micro-survey feedback platform.

Snowflake: Cloud data warehouse with separated compute and storage.

How Delighted entities map to Snowflake

Delighted entitySnowflake objectNotes
surveysdelighted_surveysid PK · custom fields → VARIANT columns
responsesdelighted_responsesid PK · linked to delighted_surveys
scoresdelighted_scoresid PK · linked to delighted_surveys
follow-up workflowsdelighted_follow_up_workflowsid PK · linked to delighted_surveys

FAQ

How does Datrise handle Delighted'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 Delighted 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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