DatriseAI-first ETL

Urban Airship Google BigQuery

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

How Datrise loads Urban Airship into Google BigQuery

Datrise syncs Urban Airship'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Urban Airship entities map to Google BigQuery

Urban Airship entityGoogle BigQuery objectNotes
recordsurban_airship_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsurban_airship_eventsTIMESTAMP events
configuration objectsurban_airship_configuration_objectsid PK · linked to urban_airship_records

FAQ

How does Datrise handle Urban Airship'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 Urban Airship 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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