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Das Örtliche Scraper

Turn Das Örtliche (dasoertliche.de) into a clean, structured German business lead and company intelligence dataset.

This actor is built for users who want to extract high-quality local business listings — including phone numbers, addresses, websites, opening hours, and rich profile data — in a format that's immediately usable for analysis, outreach, or automation.

🚀 Built for agencies & data workflows

This scraper focuses on reliability, consistency, and maintainability, making it ideal for production pipelines and recurring data collection.

• One listing = one row Each business is emitted once and deduplicated automatically, ensuring clean datasets across runs.

• Structured, canonical output All data is normalized into a consistent schema, making it easy to combine with other sources or plug into existing pipelines.

• Rich contact information Phone numbers, addresses, websites, and additional links are extracted wherever available.

• Deep profile extraction Includes opening hours, taxonomies, services, media, and other structured metadata when present.

• No hard page limit Automatically paginates through all available results and stops only when the directory is exhausted.

• Built for unattended runs Handles missing fields, partial failures, and layout changes gracefully. Logs unknown page changes for fast debugging and maintenance.

• Clean exports by default Works out of the box with CSV, Excel, JSON, XML, and Apify's table view.

🎯 Typical use cases

• Local Lead Generation – Build German business lead lists by category and location
• Sales & Marketing – Extract verified contact data for outreach campaigns
• Market Research – Analyze industries, services, and local competition
• Data Enrichment – Augment existing datasets with structured business data
• Automation Pipelines – Feed normalized business data into CRMs or internal tools
• Reverse Phone Lookup – Resolve a phone number to a full business identity, including address, hours, and contact details

⚙️ Input

Standard search

{
"query":"Elektriker",
"location":"Hamburg"
}

Reverse phone lookup

Pass a phone number as the query — the scraper detects it automatically and resolves it to a business. No location needed.

{
"query":"+49 40 123456"
}

📦 Output: canonical business data

Each dataset row represents one business listing, normalized into a consistent, schema-validated structure.

The output is designed for both:

• Flat usage (spreadsheets, CRMs)
• Nested usage (APIs, automation pipelines)

⭐ Commonly used fields

• name
• address
• phones[]
• links[] (website, email, etc.)
• openingHours
• services

📘 Schema overview

All records follow a canonical business schema, allowing seamless integration across multiple data sources.

• Identity & provenance
  • provenance.sourceId – Always dasoertliche.de
  • provenance.sourceUrl – Original listing URL
  • scrapedAt – Timestamp of extraction
• Contact & location
  • address – Structured address fields
  • phones[] – Normalized phone numbers (raw and parsed to E.164)
  • links[] – Website, email, and other links
  • social[] – Social media profiles (if available)
• Operations
  • openingHours[] – Structured weekly schedule
• Business content
  • taxonomies – Source-specific categories and tags
  • services, products, brands – Extracted where available
• Reputation
  • ratings[] – Aggregated ratings from various sources
  • reviews[] – Individual reviews (if present)
• Media & extras
  • media[] – Images and videos
  • faq[] – Structured Q&A content
  • extra – Source-specific data not covered by the schema

🛡️ Reliability & guarantees

• Strongly typed, schema-validated output
• Automatic recovery from transient errors
• Safe to stop and restart without duplicating results
• Continues scraping even if individual fields are missing
• Logs structural changes for quick scraper updates

👥 Target audience

This scraper is designed for:

• Agencies & Sales Teams – Build high-quality local lead lists
• Business Owners – Research competitors and local markets
• Data Teams – Integrate structured business data into pipelines
• Automation Builders – Use normalized output across multiple sources

💬 Support

Questions, edge cases, or unexpected output?

Attach a run log or example URL — fixes are usually quick.