DatriseAI-first ETL

My Hours to Azure Data Lake Storage Integration

AI-first ETL from My Hours into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads My Hours into Azure Data Lake Storage

Datrise syncs My Hours's time logs, projects, tasks, clients, team members, and billable rates into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.

Ideal for Azure lakehouse storage shared across Synapse and Databricks.

Endpoints

My Hours: Time tracking and billable hours for projects.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How My Hours entities map to Azure Data Lake Storage

My Hours entityAzure Data Lake Storage objectNotes
time logsmy_hours_time_logsid PK · custom fields → nested struct/map fields in Parquet
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 Azure Data Lake Storage?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.

How does the My Hours to Azure Data Lake Storage sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.

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