DatriseAI-first ETL

Adjust Google BigQuery

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

How Datrise loads Adjust into Google BigQuery

Datrise syncs Adjust's records, events, and configuration objects 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

Adjust: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Adjust entities map to Google BigQuery

Adjust entityGoogle BigQuery objectNotes
recordsadjust_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsadjust_eventsTIMESTAMP events
configuration objectsadjust_configuration_objectsid PK · linked to adjust_records

FAQ

How does Datrise handle Adjust'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 Adjust 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

Connect Adjust to Google BigQuery the easy way

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