Apify to Spotfire Integration
AI-first ETL from Apify into Spotfire. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Spotfire
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.
Ideal for interactive analytical visualization and data science.
Endpoints
Apify: Web scraping and browser automation platform.
Spotfire: Visual analytics platform for interactive dashboards and data science workflows.
How Apify entities map to Spotfire
| Apify entity | Spotfire object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened columns for visualizations |
| 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 Spotfire?
Flexible values are stored as flattened columns for visualizations, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Spotfire types.
How does the Apify to Spotfire sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.
Related pipelines
More destinations for Apify
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