DatriseAI-first ETL

Ip2whois Databricks SQL Warehouse

AI-first ETL from Ip2whois into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Ip2whois into Databricks SQL Warehouse

Datrise syncs Ip2whois's records, events, and configuration objects into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

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

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Ip2whois entities map to Databricks SQL Warehouse

Ip2whois entityDatabricks SQL Warehouse objectNotes
recordsip2whois_recordsid PK · custom fields → VARIANT or STRUCT columns
eventsip2whois_eventsTIMESTAMP events
configuration objectsip2whois_configuration_objectsid PK · linked to ip2whois_records

FAQ

How does Datrise handle Ip2whois's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the Ip2whois to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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