YouTube Analytics to GoodData Integration
AI-first ETL from YouTube Analytics into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads YouTube Analytics into GoodData
Datrise syncs YouTube Analytics's videos, channels, views, watch time, subscribers, and traffic sources into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.
Ideal for embedded, multi-tenant analytics.
Endpoints
YouTube Analytics: Channel and video performance reporting.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How YouTube Analytics entities map to GoodData
| YouTube Analytics entity | GoodData object | Notes |
|---|---|---|
| videos | youtube_analytics_videos | id PK · custom fields → flattened columns |
| channels | youtube_analytics_channels | id PK · linked to youtube_analytics_videos |
| views | youtube_analytics_views | id PK · linked to youtube_analytics_videos |
| watch time | youtube_analytics_watch_time | id PK · linked to youtube_analytics_videos |
FAQ
How does Datrise handle YouTube Analytics's custom fields in GoodData?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native GoodData types.
How does the YouTube Analytics to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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- YouTube Analytics → Databricks SQL Warehouse
More sources for GoodData
Early access
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