DatriseAI-first ETL

Primetric to Google BigQuery Integration

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

How Datrise loads Primetric into Google BigQuery

Datrise syncs Primetric's projects, assignments, people, timesheets, budgets, and utilization rates 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

Primetric: Project profitability and resource forecasting.

Google BigQuery: Serverless analytics warehouse on GCP.

How Primetric entities map to Google BigQuery

Primetric entityGoogle BigQuery objectNotes
projectsprimetric_projectsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
assignmentsprimetric_assignmentsid PK · linked to primetric_projects
peopleprimetric_peopleid PK · linked to primetric_projects
timesheetsprimetric_timesheetsid PK · linked to primetric_projects

FAQ

How does Datrise handle Primetric'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 Primetric 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

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