DatriseAI-first ETL

Apollo Google BigQuery

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

How Datrise loads Apollo into Google BigQuery

Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity 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

Apollo: Sales intelligence and engagement platform with account-level activity.

Google BigQuery: Serverless analytics warehouse on GCP.

How Apollo entities map to Google BigQuery

Apollo entityGoogle BigQuery objectNotes
sales intelligence recordsapollo_sales_intelligence_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
account engagementapollo_account_engagementid PK · linked to apollo_sales_intelligence_records
outbound activityapollo_outbound_activityTIMESTAMP events

FAQ

How does Datrise handle Apollo'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 Apollo 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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