DatriseAI-first ETL

Senseforce to Google BigQuery Integration

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

How Datrise loads Senseforce into Google BigQuery

Datrise syncs Senseforce's assets, sensor readings, events, machines, time series values, and alarms 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

Senseforce: Industrial IoT machine telemetry and analytics.

Google BigQuery: Serverless analytics warehouse on GCP.

How Senseforce entities map to Google BigQuery

Senseforce entityGoogle BigQuery objectNotes
assetssenseforce_assetsid PK · custom fields → JSON or nested/repeated (STRUCT) columns
sensor readingssenseforce_sensor_readingsid PK · linked to senseforce_assets
eventssenseforce_eventsTIMESTAMP events
machinessenseforce_machinesid PK · linked to senseforce_assets

FAQ

How does Datrise handle Senseforce'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 Senseforce 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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