Apify to Mode Integration
AI-first ETL from Apify into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apify into Mode
Datrise syncs Apify's actors, runs, datasets, key-value stores, request queues, and run logs into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Apify: Web scraping and browser automation platform.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Apify entities map to Mode
| Apify entity | Mode object | Notes |
|---|---|---|
| actors | apify_actors | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Apify to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Apify
Early access
Connect Apify to Mode the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.