My Hours to Snowflake Integration
AI-first ETL from My Hours into Snowflake. Governed entities, incremental sync, typed landing tables.
How Datrise loads My Hours into Snowflake
Datrise syncs My Hours's time logs, projects, tasks, clients, team members, and billable rates into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.
Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.
Ideal for central analytics warehouses feeding BI and AI workloads.
Endpoints
My Hours: Time tracking and billable hours for projects.
Snowflake: Cloud data warehouse with separated compute and storage.
How My Hours entities map to Snowflake
| My Hours entity | Snowflake object | Notes |
|---|---|---|
| time logs | my_hours_time_logs | id PK · custom fields → VARIANT columns |
| projects | my_hours_projects | id PK · linked to my_hours_time_logs |
| tasks | my_hours_tasks | id PK · linked to my_hours_time_logs |
| clients | my_hours_clients | id PK · linked to my_hours_time_logs |
FAQ
How does Datrise handle My Hours's custom fields in Snowflake?
Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.
How does the My Hours to Snowflake sync stay up to date?
It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.
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