DatriseAI-first ETL

Pendo Google BigQuery

AI-first ETL from Pendo into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Pendo into Google BigQuery

Datrise syncs Pendo's events, guides, NPS, feature adoption, and account metadata into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Pendo entities map to Google BigQuery

Pendo entityGoogle BigQuery 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 Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the Pendo to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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