DatriseAI-first ETL

Pendo Databricks SQL Warehouse

AI-first ETL from Pendo into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Pendo into Databricks SQL Warehouse

Datrise syncs Pendo's events, guides, NPS, feature adoption, and account metadata into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

Pendo: Product analytics and in-app guidance for SaaS teams.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Pendo entities map to Databricks SQL Warehouse

Pendo entityDatabricks SQL Warehouse objectNotes
eventspendo_eventsTIMESTAMP events
guidespendo_guidesid PK · linked to pendo_events
NPSpendo_npsid PK · linked to pendo_events
feature adoptionpendo_feature_adoptionid PK · linked to pendo_events

FAQ

How does Datrise handle Pendo's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Pendo to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

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