Make.com + Apify: Automate Web Scraping Workflows Without Code
Most teams can scrape data—the bottleneck is moving output into business workflows reliably. Make orchestrates Apify Actors: run on schedule, fetch datasets, transform records, and route to Sheets, CRM, or Slack. This guide covers setup, modules, and quality controls.
What the integration supports
Make's Apify modules include:
| Module | Purpose |
|---|---|
| Run an Actor | Direct actor invocation |
| Run a Task | Preconfigured, repeatable runs |
| Get Dataset Items | Fetch results after run |
| Watch Actor Runs | Event-driven trigger |
| Watch Task Runs | Event-driven trigger |
| Scrape Single URL | Quick one-off extraction |
| Make an API Call | Custom Apify API usage |
Minimum viable pipeline
- Trigger — Schedule or upstream event
- Run Apify Actor/Task
- Wait for completion (or use Watch Runs trigger)
- Fetch dataset items
- Iterate + transform records
- Send to destination (Sheets, CRM, Slack, DB)
Step-by-step setup
Step 1: Connect Apify in Make
- Add Apify module in a new scenario
- Authenticate with Apify API token
- Test with a simple Run Actor call (e.g., Web Scraper)
Step 2: Choose run mode
| Mode | Use when |
|---|---|
| Run an Actor | Direct invocation; dynamic inputs |
| Run a Task | Repeatable config; easier governance |
For production, Tasks are often easier to manage.
Step 3: Retrieve results
After run completion, use Get Dataset Items.
- Start with small output limits while testing
- Validate schema before mapping to destinations
Step 4: Transform and route
Use iterators and filters to:
- Keep only required fields
- Normalize text, date, price values
- Split records by destination rules
Step 5: Deliver to business tools
Common destinations:
- Google Sheets — Quick ops visibility
- HubSpot/Salesforce — Lead workflows
- Slack — Alerts
- Database — Downstream analytics
Example scenario: Google Maps leads
| Step | Action |
|---|---|
| 1 | Schedule daily run |
| 2 | Run Apify Google Maps Scraper |
| 3 | Get Dataset Items in Make |
| 4 | Filter by region and category |
| 5 | Send qualified rows to CRM |
| 6 | Notify sales in Slack |
Quality controls
| Control | Purpose |
|---|---|
| Duplicate check | Before write modules |
| Required fields | Reject rows missing domain, contact, location |
| Confidence flags | For enrichment outputs |
| Run metadata | Store run_id, source actor, timestamp |
Compliance
- Respect site policies and lawful use
- Minimize personal data; keep purpose-specific
- Apply retention rules by jurisdiction
- Route records by region for compliant outreach
Troubleshooting
| Issue | Fix |
|---|---|
| Actor runs but no rows | Verify dataset reference; check actor output structure |
| Too many rows | Tighten actor input filters |
| Mapping errors | Normalize schema before write |
| Cost growth | Filter earlier; reduce unnecessary modules |
Start your Make + Apify scenario.
Once your pipeline is stable, add one AI classification step for prioritization. Build in Make →
Yes. Use Run Actor or Run Task modules. Process results in the same scenario with Get Dataset Items and iterators.
Use Get Dataset Items, then iterate and map only the fields your destination needs. Filter before heavy processing.
For many workflows, yes. You can build full scrape-to-destination automations visually in Make.
Any Actor with dataset output. Popular: Google Maps, SERP, LinkedIn, e-commerce, job listings. Browse apify.com/store.