DatriseAI-first ETL

Nimble Google BigQuery

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

How Datrise loads Nimble into Google BigQuery

Datrise syncs Nimble's relationship records, deals, tasks, and activity intelligence 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

Nimble: Relationship-focused CRM for SMB sales teams.

Google BigQuery: Serverless analytics warehouse on GCP.

How Nimble entities map to Google BigQuery

Nimble entityGoogle BigQuery objectNotes
relationship recordsnimble_relationship_recordsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
dealsnimble_dealsid PK · linked to nimble_relationship_records
tasksnimble_tasksid PK · linked to nimble_relationship_records
activity intelligencenimble_activity_intelligenceTIMESTAMP events

FAQ

How does Datrise handle Nimble'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 Nimble 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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