Pendo → Mode
AI-first ETL from Pendo into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Pendo into Mode
Datrise syncs Pendo's events, guides, NPS, feature adoption, and account metadata 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
Pendo: Product analytics and in-app guidance for SaaS teams.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Pendo entities map to Mode
| Pendo entity | Mode object | Notes |
|---|---|---|
| events | pendo_events | temporal columns events |
| guides | pendo_guides | id PK · linked to pendo_events |
| NPS | pendo_nps | id PK · linked to pendo_events |
| feature adoption | pendo_feature_adoption | id PK · linked to pendo_events |
FAQ
How does Datrise handle Pendo'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 Pendo to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
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Early access
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