Strava → Mode
AI-first ETL from Strava into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Strava into Mode
Datrise syncs Strava's records, events, and configuration objects into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Strava: SaaS or API data source for analytics and warehouse sync.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Strava entities map to Mode
| Strava entity | Mode object | Notes |
|---|---|---|
| records | strava_records | id PK · custom fields → flattened columns for SQL and notebooks |
| events | strava_events | temporal columns events |
| configuration objects | strava_configuration_objects | id PK · linked to strava_records |
FAQ
How does Datrise handle Strava's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Strava to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Strava
Early access
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