Smaily → Looker Studio
AI-first ETL from Smaily into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Smaily into Looker Studio
Datrise syncs Smaily's records, events, and configuration objects into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Smaily: SaaS or API data source for analytics and warehouse sync.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Smaily entities map to Looker Studio
| Smaily entity | Looker Studio object | Notes |
|---|---|---|
| records | smaily_records | id PK · custom fields → flattened columns for chart fields |
| events | smaily_events | date dimension columns events |
| configuration objects | smaily_configuration_objects | id PK · linked to smaily_records |
FAQ
How does Datrise handle Smaily's custom fields in Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Smaily to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Smaily to Looker Studio the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.