DatriseAI-first ETL

Yandex Metrica Chartio

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

How Datrise loads Yandex Metrica into Chartio

Datrise syncs Yandex Metrica's records, events, and configuration objects into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.

Ideal for drag-and-drop charting over a database.

Endpoints

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

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Yandex Metrica entities map to Chartio

Yandex Metrica entityChartio objectNotes
recordsyandex_metrica_recordsid PK · custom fields → flattened columns for visual SQL
eventsyandex_metrica_eventstemporal columns events
configuration objectsyandex_metrica_configuration_objectsid PK · linked to yandex_metrica_records

FAQ

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

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

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

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

Related pipelines

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