We extract local business profiles, rating aggregates, review text, operating hours, and service menus from Yelp. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Business Profiles objects from yelp.com. All fields typed and schema-versioned.
"business_id": "b_1294819", "name": "Tartine Bakery", "rating": 4.5, "review_count": 8492, "claimed_status": true, "price_range": "$$", "city": "San Francisco", "health_score": 94
| # | business_id | name | alias | phone | display_phone | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from yelp.com. All fields typed and schema-versioned.
"review_id": "r_9481029", "business_id": "b_1294819", "user_name": "Sarah M.", "user_elite_status": true, "rating": 5, "date": "2026-03-14", "useful_votes": 12, "owner_response": "None"
| # | review_id | business_id | user_id | user_name | user_elite_status | user_review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Operating Hours objects from yelp.com. All fields typed and schema-versioned.
"business_id": "b_1294819", "day_of_week": "Monday", "open_time": "08:00", "close_time": "17:00", "is_closed": false, "special_hours_date": "None"
| # | business_id | day_of_week | open_time | close_time | is_overnight | is_closed |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Services & Menus objects from yelp.com. All fields typed and schema-versioned.
"business_id": "b_1294819", "item_name": "Morning Bun", "item_description": "Flaky croissant dough with cinnamon and orange zest.", "item_price": 5.5, "section_name": "Pastries", "menu_name": "Breakfast"
| # | business_id | item_id | item_name | item_description | item_price | section_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Search Results objects from yelp.com. All fields typed and schema-versioned.
"keyword": "bakery", "location": "San Francisco, CA", "position": 1, "business_id": "b_1294819", "is_sponsored": false, "rating": 4.5
| # | keyword | location | position | business_id | name | rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Yelp scraper handles every layer of the directory: business listings, dynamic search rankings, review pagination, and image metadata. Built with JavaScript rendering and IP rotation to bypass bot protection.
Name, address, coordinates, phone numbers, claimed status, and price tiers scraped directly from business pages.
Extract full review text, star ratings, vote counts, and owner responses across hundreds of paginated pages.
Identify reviews from Yelp Elite squad members, including their historical review counts and user metadata.
Capture standard weekly hours alongside holiday exceptions and special event closures.
Extract structured menu items, pricing, category sections, and service lists for restaurants and contractors.
Track organic versus sponsored positions for specific keywords across targeted postal codes and cities.
Capture municipal health inspection scores, accessibility features, and accepted payment methods.
Monitor aggregate rating shifts and review velocity to identify trending businesses or declining service quality.
Run continuous pipelines that only output changed records, reducing downstream processing load.
Brief in. Clean data out.
Provide geographic bounding boxes, category lists, or specific business IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and CAPTCHA handling for yelp.com.
Schema validation, null-rate checks, and sample reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Yelp employs aggressive rate limiting and bot detection. Here is how we maintain data flow.
Yelp uses advanced fingerprinting and IP reputation scoring. We route requests through ISP-grade residential proxies with rotated browser fingerprints to mimic organic human traffic.
Yelp obfuscates CSS classes regularly. Our extraction logic relies on structural DOM relationships and JSON-LD metadata rather than brittle class selectors.
Yelp caps search results at 240 items. We automatically subdivide geographic search grids into micro-zones to ensure 100% coverage of dense urban areas.
Many amenities and dynamic operating hours require JavaScript execution. We run headless Playwright sessions to capture data hidden from standard HTTP clients.
Yelp defaults to 'Yelp Sort'. We force chronological sorting parameters to ensure incremental pipelines only fetch newly published reviews.
Agencies track search visibility, review sentiment, and competitor rankings across specific postal codes.
B2B sales teams extract newly listed businesses, claimed status, and contact details to build targeted outreach lists.
Data science teams ingest review text to train NLP models on consumer sentiment and service feedback.
Retail strategists analyse category density and rating distributions to identify underserved neighbourhoods for expansion.
Franchise operators monitor review velocity and rating trends across competing regional locations.
Private equity firms track foot traffic proxies via review volume growth to evaluate local business acquisitions.
"Yelp contains the most accurate ground-truth data for local commerce, but extracting it requires navigating aggressive bot protection and complex pagination."
Most teams fail at scraping Yelp because they rely on datacenter IPs and static selectors. DataFlirt manages the residential proxy pools, JavaScript rendering, and CAPTCHA solving required to maintain a reliable stream of local business data. You receive clean, normalised records ready for analysis.
Everything supported by our yelp.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering and session interaction.
We maintain pools of residential ISP proxies. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About yelp.com scraping, legality, and pipeline operations.
Ask us directly →We programmatically divide large geographic areas into smaller coordinate bounding boxes, ensuring every sub-grid returns fewer than 240 results. This guarantees complete extraction of dense urban areas.
We extract reviews visible on the main profile and can explicitly target the 'not recommended' review section if required by your schema.
Pipelines can be configured to run daily or weekly. We track changes and only emit records when operating hours or special event schedules are updated.
We extract public metadata attached to reviews, such as user names, Elite status, and review counts. We do not extract private user data or scrape individual user profile pages.
We support all geographic regions covered by Yelp, including North America, Europe, and Asia-Pacific. Search queries can be targeted by city, postal code, or exact coordinates.
Our infrastructure uses a combination of optimal request timing, residential IPs, and automated CAPTCHA solvers (CapSolver/2Captcha) to maintain pipeline throughput without manual intervention.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off business directory dump or continuous review monitoring across 50 cities, we build and operate the pipeline. Tell us what you need.
