DatriseAI-first ETL

Yandex Metrica Looker

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

How Datrise loads Yandex Metrica into Looker

Datrise syncs Yandex Metrica's records, events, and configuration objects into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.

Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.

Ideal for governed, version-controlled BI on a warehouse.

Endpoints

Yandex Metrica: SaaS or API data source for analytics and warehouse sync.

Looker: Google Cloud BI with LookML semantic models and governed dashboards.

How Yandex Metrica entities map to Looker

Yandex Metrica entityLooker objectNotes
recordsyandex_metrica_recordsid PK · custom fields → flattened columns (nested fields expanded for modeling)
eventsyandex_metrica_eventsdate/time dimension columns events
configuration objectsyandex_metrica_configuration_objectsid PK · linked to yandex_metrica_records

FAQ

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

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

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

It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.

Related pipelines

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