DatriseAI-first ETL

Listrak Google BigQuery

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

How Datrise loads Listrak into Google BigQuery

Datrise syncs Listrak'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Listrak entities map to Google BigQuery

Listrak entityGoogle BigQuery objectNotes
recordslistrak_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventslistrak_eventsTIMESTAMP events
configuration objectslistrak_configuration_objectsid PK · linked to listrak_records

FAQ

How does Datrise handle Listrak'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 Listrak 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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