Yandex Metrica to DuckDB Integration

How Datrise loads Yandex Metrica into DuckDB

Datrise syncs Yandex Metrica's sessions, visitors, pageviews, traffic sources, goals, and user parameters into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Yandex Metrica: Website traffic analytics and session recordings.

DuckDB: In-process analytics database for fast local OLAP.

How Yandex Metrica entities map to DuckDB

Yandex Metrica entity	DuckDB object	Notes
sessions	`yandex_metrica_sessions`	id PK · custom fields → JSON or STRUCT columns
visitors	`yandex_metrica_visitors`	id PK · linked to yandex_metrica_sessions
pageviews	`yandex_metrica_pageviews`	id PK · linked to yandex_metrica_sessions
traffic sources	`yandex_metrica_traffic_sources`	id PK · linked to yandex_metrica_sessions

FAQ

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

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

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

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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