DatriseAI-first ETL

Pepperjam to Google BigQuery Integration

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

How Datrise loads Pepperjam into Google BigQuery

Datrise syncs Pepperjam's publishers, transactions, clicks, commissions, creatives, and programs 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

Pepperjam: Affiliate network performance and publisher payouts.

Google BigQuery: Serverless analytics warehouse on GCP.

How Pepperjam entities map to Google BigQuery

Pepperjam entityGoogle BigQuery objectNotes
publisherspepperjam_publishersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
transactionspepperjam_transactionsid PK · linked to pepperjam_publishers
clickspepperjam_clicksid PK · linked to pepperjam_publishers
commissionspepperjam_commissionsid PK · linked to pepperjam_publishers

FAQ

How does Datrise handle Pepperjam'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 Pepperjam 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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