Delighted → Mode
AI-first ETL from Delighted into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Delighted into Mode
Datrise syncs Delighted's surveys, responses, scores, and follow-up workflows into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Delighted: NPS and micro-survey feedback platform.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Delighted entities map to Mode
| Delighted entity | Mode object | Notes |
|---|---|---|
| surveys | delighted_surveys | id PK · custom fields → flattened columns for SQL and notebooks |
| responses | delighted_responses | id PK · linked to delighted_surveys |
| scores | delighted_scores | id PK · linked to delighted_surveys |
| follow-up workflows | delighted_follow_up_workflows | id PK · linked to delighted_surveys |
FAQ
How does Datrise handle Delighted's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Delighted to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Delighted
Early access
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