DatriseAI-first ETL

Yandex Metrica Birst

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

How Datrise loads Yandex Metrica into Birst

Datrise syncs Yandex Metrica's records, events, and configuration objects into Birst as warehouse tables for Birst's automated star schema. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the source tables Birst ingests, so re-runs update only what changed. Date-partitioned facts. Birst builds its own semantic layer, so Datrise lands conformed, well-keyed tables it can automate against.

Ideal for networked, governed enterprise BI.

Endpoints

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

Birst: Cloud BI with networked analytics and enterprise semantic layers.

How Yandex Metrica entities map to Birst

Yandex Metrica entityBirst objectNotes
recordsyandex_metrica_recordsid PK · custom fields → flattened columns
eventsyandex_metrica_eventsdate/time dimensions events
configuration objectsyandex_metrica_configuration_objectsid PK · linked to yandex_metrica_records

FAQ

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

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 Birst types.

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

It runs incrementally — Datrise uses incremental refresh of the source tables Birst ingests.

Related pipelines

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