DatriseAI-first ETL

Outreach Google BigQuery

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

How Datrise loads Outreach into Google BigQuery

Datrise syncs Outreach's sequence activity, pipeline execution metrics, and sales engagement events 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

Outreach: Sales execution platform for sequence activity and pipeline outcomes.

Google BigQuery: Serverless analytics warehouse on GCP.

How Outreach entities map to Google BigQuery

Outreach entityGoogle BigQuery objectNotes
sequence activityoutreach_sequence_activityTIMESTAMP events
pipeline execution metricsoutreach_pipeline_execution_metricsid PK · linked to outreach_sequence_activity
sales engagement eventsoutreach_sales_engagement_eventsTIMESTAMP events

FAQ

How does Datrise handle Outreach'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 Outreach 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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