Apify to Looker Studio Integration

How Datrise loads Apify into Looker Studio

Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs 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

Apify: Web scraping and browser automation platform.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Apify entities map to Looker Studio

Apify entity	Looker Studio object	Notes
actors	`apify_actors`	id PK · custom fields → flattened columns for chart fields
runs	`apify_runs`	id PK · linked to apify_actors
datasets	`apify_datasets`	id PK · linked to apify_actors
key-value stores	`apify_key_value_stores`	id PK · linked to apify_actors

FAQ

How does Datrise handle Apify'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 Apify to Looker Studio sync stay up to date?

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

Related pipelines

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