Apify to Tableau Integration

How Datrise loads Apify into Tableau

Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Apify: Web scraping and browser automation platform.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Apify entities map to Tableau

Apify entity	Tableau object	Notes
actors	`apify_actors`	id PK · custom fields → flattened columns for Tableau 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 Tableau?

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

How does the Apify to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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