DatriseAI-first ETL

Ip2whois Google BigQuery

AI-first ETL from Ip2whois into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads Ip2whois into Google BigQuery

Datrise syncs Ip2whois'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

Ip2whois: SaaS or API data source for analytics and warehouse sync.

Google BigQuery: Serverless analytics warehouse on GCP.

How Ip2whois entities map to Google BigQuery

Ip2whois entityGoogle BigQuery objectNotes
recordsip2whois_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
eventsip2whois_eventsTIMESTAMP events
configuration objectsip2whois_configuration_objectsid PK · linked to ip2whois_records

FAQ

How does Datrise handle Ip2whois'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 Ip2whois 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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