DatriseAI-first ETL

Ploomes Google BigQuery

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

How Datrise loads Ploomes into Google BigQuery

Datrise syncs Ploomes's contacts, accounts, deals, activities, and lifecycle events 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

Ploomes: CRM widely used in Latin America for sales pipeline and customer ops.

Google BigQuery: Serverless analytics warehouse on GCP.

How Ploomes entities map to Google BigQuery

Ploomes entityGoogle BigQuery objectNotes
contactsploomes_contactsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
accountsploomes_accountsid PK · linked to ploomes_contacts
dealsploomes_dealsid PK · linked to ploomes_contacts
activitiesploomes_activitiesTIMESTAMP events

FAQ

How does Datrise handle Ploomes'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 Ploomes 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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