DatriseAI-first ETL

My Hours to Databricks SQL Warehouse Integration

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

How Datrise loads My Hours into Databricks SQL Warehouse

Datrise syncs My Hours's time logs, projects, tasks, clients, team members, and billable rates 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

My Hours: Time tracking and billable hours for projects.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How My Hours entities map to Databricks SQL Warehouse

My Hours entityDatabricks SQL Warehouse objectNotes
time logsmy_hours_time_logsid PK · custom fields → VARIANT or STRUCT columns
projectsmy_hours_projectsid PK · linked to my_hours_time_logs
tasksmy_hours_tasksid PK · linked to my_hours_time_logs
clientsmy_hours_clientsid PK · linked to my_hours_time_logs

FAQ

How does Datrise handle My Hours'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 My Hours 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.

Related pipelines

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