DatriseAI-first ETL

Yandex Metrica to Snowflake Integration

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

How Datrise loads Yandex Metrica into Snowflake

Datrise syncs Yandex Metrica's sessions, visitors, pageviews, traffic sources, goals, and user parameters into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Yandex Metrica: Website traffic analytics and session recordings.

Snowflake: Cloud data warehouse with separated compute and storage.

How Yandex Metrica entities map to Snowflake

Yandex Metrica entitySnowflake objectNotes
sessionsyandex_metrica_sessionsid PK · custom fields → VARIANT columns
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 Snowflake?

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

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

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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