DatriseAI-first ETL

Tempo to Databricks SQL Warehouse Integration

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

How Datrise loads Tempo into Databricks SQL Warehouse

Datrise syncs Tempo's worklogs, accounts, projects, users, teams, 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

Tempo: Time tracking and worklogs layered on Jira.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Tempo entities map to Databricks SQL Warehouse

Tempo entityDatabricks SQL Warehouse objectNotes
worklogstempo_worklogsid PK · custom fields → VARIANT or STRUCT columns
accountstempo_accountsid PK · linked to tempo_worklogs
projectstempo_projectsid PK · linked to tempo_worklogs
userstempo_usersid PK · linked to tempo_worklogs

FAQ

How does Datrise handle Tempo'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 Tempo 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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