We extract price comparisons, merchant listings, product specifications, shipping estimates, and reviews from Google Shopping. 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 Product Specifications objects from Google Shopping. All fields typed and schema-versioned.
"product_id": "1234567890123456789", "title": "Google Pixel 8 Pro", "brand": "Google", "gtin": "00193575035223", "rating": 4.6, "review_count": 3482, "specifications": "['Display: 6.7 inches', 'Storage: 128GB']"
| # | product_id | title | brand | category | description | gtin |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Price Comparisons objects from Google Shopping. All fields typed and schema-versioned.
"product_id": "1234567890123456789", "merchant_name": "Best Buy", "base_price": 999.0, "shipping_cost": 0.0, "total_price": 999.0, "condition": "New", "availability": "In stock", "timestamp": "2026-05-12T10:14:00Z"
| # | product_id | merchant_name | merchant_url | base_price | total_price | shipping_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results (SERP) objects from Google Shopping. All fields typed and schema-versioned.
"keyword": "pixel 8 pro", "position": 3, "product_id": "1234567890123456789", "sponsored": false, "price": 999.0, "merchant": "Google Store", "scraped_at": "2026-05-12T10:15:33Z"
| # | keyword | location | position | product_id | title | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Merchant Data objects from Google Shopping. All fields typed and schema-versioned.
"merchant_name": "Best Buy", "merchant_rating": 4.5, "review_count": 150000, "return_policy": "15-day returns", "free_shipping_threshold": 35.0, "certifications": "['Google Trusted Store']"
| # | merchant_id | merchant_name | merchant_rating | review_count | return_policy | free_shipping_threshold |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Reviews objects from Google Shopping. All fields typed and schema-versioned.
"product_id": "1234567890123456789", "star_rating": 5, "review_title": "Great camera", "review_body": "The AI features are amazing.", "source_site": "Best Buy", "review_date": "2026-04-18"
| # | review_id | product_id | reviewer_name | star_rating | review_title | review_body |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Google Shopping scraper handles the complexities of location-based pricing, dynamic merchant lists, and pagination — with JavaScript rendering, proxy rotation, and anti-bot circumvention built in.
Title, specifications, images, and brand details — scraped at the product ID level with GTIN/UPC mapping.
Capture base price, shipping costs, tax estimates, total price, and condition across all merchants listing a product.
Extract merchant names, ratings, return policies, and Buy on Google eligibility for every offer.
Full review text, star ratings, and source site attribution — capturing Google's aggregated review corpus.
Track organic vs sponsored positions for any keyword, location, and device type in Google Shopping results.
Simulate searches from specific zip codes or cities to capture local inventory and regional pricing differences.
google.com, google.co.uk, google.de, google.co.in and other regional domains — all from a unified schema.
Monitor pricing differences across new, used, and refurbished conditions, as well as color/size variants.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide keywords, product URLs, or GTINs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for Google Shopping.
Schema validation, null-rate checks, price-outlier detection, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Google constantly updates its DOM and aggressively blocks automated traffic. Here's how we stay resilient.
Google uses advanced fingerprinting and reCAPTCHA. Our crawlers use residential ISP proxies with realistic browser fingerprints and automated CAPTCHA solving to maintain throughput.
Google Shopping relies heavily on JavaScript for loading merchant lists, reviews, and interactive elements. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
Prices and availability vary by location. We configure proxies and browser geolocation APIs to simulate searches from specific zip codes, ensuring accurate local inventory and shipping costs.
Google frequently A/B tests and changes its DOM structure. Our selector strategy uses multiple fallback chains per field, including parsing embedded JSON-LD where available.
For large product catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost, storage bloat, and downstream processing load.
Retailers monitor competitor pricing, shipping costs, and promotions across Google Shopping to adjust their own pricing strategies.
Brands track merchant listings to identify Minimum Advertised Price (MAP) violations and unauthorised sellers across the web.
Analysts track product visibility, pricing trends, and review sentiment to understand market dynamics and consumer preferences.
Marketing teams track organic vs sponsored placements to optimise their Google Merchant Center feeds and ad bidding strategies.
eCommerce platforms use Google Shopping data to enrich their own catalogues with standardized specifications, images, and aggregated reviews.
Retailers monitor competitor local inventory availability and pricing by scraping location-specific results.
"Google Shopping aggregates the entire retail web into a single interface. Extracting it gives you the ultimate view of market pricing and merchant behavior."
Building a reliable Google Shopping scraper requires overcoming aggressive CAPTCHAs, handling complex location-based dynamic content, and maintaining fragile selectors across frequent A/B tests. DataFlirt manages this infrastructure so you receive clean, structured pricing and product data without the engineering overhead.
Everything supported by our Google Shopping 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About Google Shopping scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Google Shopping is generally permissible under applicable law, focusing on public, non-authenticated product and pricing data. DataFlirt does not extract personal data or circumvent authentication walls. Clients should review Google's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and automated integration with CAPTCHA solving services like CapSolver to maintain pipeline throughput.
Yes. We configure proxies and browser settings to simulate searches from specific zip codes or cities, allowing us to capture accurate local inventory, pricing, and shipping costs.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined product set. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size.
Yes. We extract the full list of merchants offering a product, including base price, shipping cost, total price, condition, and merchant rating.
Our smallest packages start at a defined keyword or product list (typically 1,000-50,000 queries) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Yes. Where Google Shopping exposes GTINs, UPCs, or MPNs in the product specifications or embedded JSON-LD, we extract them to facilitate precise product matching.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off product catalogue dump or a continuous price-monitoring feed across thousands of queries — we scope, build, and operate the pipeline. Tell us what you need.
