DatriseAI-first ETL

Senseforce to Databricks SQL Warehouse Integration

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

How Datrise loads Senseforce into Databricks SQL Warehouse

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

Senseforce: Industrial IoT machine telemetry and analytics.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Senseforce entities map to Databricks SQL Warehouse

Senseforce entityDatabricks SQL Warehouse objectNotes
assetssenseforce_assetsid PK · custom fields → VARIANT or 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 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 Senseforce 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.

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