DatriseAI-first ETL

Yandex Metrica to Looker Studio Integration

AI-first ETL from Yandex Metrica into Looker Studio. Governed entities, incremental sync, typed landing tables.

How Datrise loads Yandex Metrica into Looker Studio

Datrise syncs Yandex Metrica's sessions, visitors, pageviews, traffic sources, goals, and user parameters into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Yandex Metrica: Website traffic analytics and session recordings.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Yandex Metrica entities map to Looker Studio

Yandex Metrica entityLooker Studio objectNotes
sessionsyandex_metrica_sessionsid PK · custom fields → flattened columns for chart fields
visitorsyandex_metrica_visitorsid PK · linked to yandex_metrica_sessions
pageviewsyandex_metrica_pageviewsid PK · linked to yandex_metrica_sessions
traffic sourcesyandex_metrica_traffic_sourcesid PK · linked to yandex_metrica_sessions

FAQ

How does Datrise handle Yandex Metrica's custom fields in Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the Yandex Metrica to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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