DatriseAI-first ETL

Timely to Databricks SQL Warehouse Integration

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

How Datrise loads Timely into Databricks SQL Warehouse

Datrise syncs Timely's time entries, projects, clients, users, tags, and billable hours 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

Timely: Automatic time tracking for teams and projects.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Timely entities map to Databricks SQL Warehouse

Timely entityDatabricks SQL Warehouse objectNotes
time entriestimely_time_entriesid PK · custom fields → VARIANT or STRUCT columns
projectstimely_projectsid PK · linked to timely_time_entries
clientstimely_clientsid PK · linked to timely_time_entries
userstimely_usersid PK · linked to timely_time_entries

FAQ

How does Datrise handle Timely'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 Timely 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

Early access

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