DatriseAI-first ETL

Yandex Metrica to Mode Integration

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

How Datrise loads Yandex Metrica into Mode

Datrise syncs Yandex Metrica's sessions, visitors, pageviews, traffic sources, goals, and user parameters 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

Yandex Metrica: Website traffic analytics and session recordings.

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Yandex Metrica entities map to Mode

Yandex Metrica entityMode objectNotes
sessionsyandex_metrica_sessionsid PK · custom fields → flattened columns for SQL and notebooks
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 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 Yandex Metrica to Mode sync stay up to date?

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

Related pipelines

Early access

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