DatriseAI-first ETL

Fastbill Google BigQuery

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

How Datrise loads Fastbill into Google BigQuery

Datrise syncs Fastbill'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

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Fastbill entities map to Google BigQuery

Fastbill entityGoogle BigQuery objectNotes
recordsfastbill_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsfastbill_eventsTIMESTAMP events
configuration objectsfastbill_configuration_objectsid PK · linked to fastbill_records

FAQ

How does Datrise handle Fastbill'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 Fastbill 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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