Apify to ThoughtSpot Integration
AI-first ETL from Apify into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into ThoughtSpot
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.
Ideal for natural-language search analytics over a warehouse.
Endpoints
Apify: Web scraping and browser automation platform.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Apify entities map to ThoughtSpot
| Apify entity | ThoughtSpot object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened columns for searchable 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 ThoughtSpot?
Flexible values are stored as flattened columns for searchable fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ThoughtSpot types.
How does the Apify to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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Early access
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