Clay to Looker Studio Integration
AI-first ETL from Clay into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Clay into Looker Studio
Datrise syncs Clay's tables, rows, enrichment runs, waterfalls, data sources, and exported lists 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
Clay: Data enrichment tables that build outbound lists.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Clay entities map to Looker Studio
| Clay entity | Looker Studio object | Notes |
|---|---|---|
| tables | clay_tables | id PK · custom fields → flattened columns for chart fields |
| rows | clay_rows | id PK · linked to clay_tables |
| enrichment runs | clay_enrichment_runs | id PK · linked to clay_tables |
| waterfalls | clay_waterfalls | id PK · linked to clay_tables |
FAQ
How does Datrise handle Clay'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 Clay to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Clay
More sources for Looker Studio
Early access
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