We extract luggage specifications, brand pricing signals, inventory levels, and customer reviews from eBags. 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 Data objects from ebags.com. All fields typed and schema-versioned.
"sku": "147230-1041", "brand": "Samsonite", "title": "Freeform Carry-On Spinner", "category": "Luggage > Carry-On", "capacity_liters": 38.5, "dimensions": "21.25 x 15.25 x 10.0 in", "weight": "6.5 lbs", "tsa_lock": true, "warranty": "10 Year Limited"
| # | sku | brand | title | category | capacity_liters | dimensions |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Pricing & Variants objects from ebags.com. All fields typed and schema-versioned.
"sku": "147230-1041", "price": 149.99, "list_price": 199.99, "currency": "USD", "discount_pct": 25, "colour_name": "Matte Black", "colour_hex": "#000000", "stock_status": "In Stock", "promotion_badge": "Clearance"
| # | sku | price | list_price | currency | discount_pct | colour_name |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from ebags.com. All fields typed and schema-versioned.
"review_id": "REV-9823471", "sku": "147230-1041", "rating": 4.8, "reviewer_name": "TravelPro99", "review_date": "2026-02-14", "title": "Perfect for short trips", "body": "Fits overhead bins perfectly. Wheels roll smoothly on carpet.", "helpful_votes": 12, "verified_buyer": true
| # | review_id | sku | rating | reviewer_name | review_date | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Category Listings objects from ebags.com. All fields typed and schema-versioned.
"category_id": "cat-backpacks-laptop", "category_name": "Laptop Backpacks", "sku": "89342-1041", "rank_position": 3, "is_sponsored": false, "filter_tags": "['15-inch laptop', 'water-resistant']", "total_results": 412, "scraped_at": "2026-05-12T10:15:00Z"
| # | category_id | category_name | sku | rank_position | is_sponsored | filter_tags |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Brand Aggregation objects from ebags.com. All fields typed and schema-versioned.
"brand_name": "American Tourister", "total_products": 184, "avg_price": 89.5, "min_price": 29.99, "max_price": 249.99, "top_categories": "['Hardside Luggage', 'Kids Luggage']", "brand_url": "https://www.ebags.com/brands/american-tourister", "active_promotions": true
| # | brand_name | total_products | avg_price | min_price | max_price | top_categories |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our eBags scraper handles every layer of the platform: product specifications, dynamic pricing, colour variants, brand intelligence, and the review corpus — with JavaScript rendering and session management built in.
Extract precise linear dimensions, weight, and volume capacities across all carry-on and checked luggage categories.
Capture base price, list price, promotional discounts, and clearance badges — timestamped per crawl.
Extract all available colourways, hex codes, and variant-specific pricing or stock statuses for a single SKU.
Full review text, star ratings, helpful vote counts, and verified buyer flags — paginated across all product reviews.
Extract material types (polycarbonate, nylon), TSA lock presence, wheel counts, and handle mechanisms.
Monitor brand-specific catalogues, mapping product distribution across Samsonite, American Tourister, Tumi, and more.
Track organic search positions within specific eBags categories to monitor product visibility.
Capture warranty terms, durations, and specific brand guarantees listed on product detail pages.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide SKU lists, category URLs, or brand names. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and session management for ebags.com.
Schema validation, null-rate checks, price-outlier detection, and sample reviews before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
eCommerce sites invest heavily in scraping detection. Here's how we stay resilient — and why teams choose managed infrastructure over DIY.
Retail bot detection operates on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints, trained on real user behaviour patterns.
eBags product pages and dynamic pricing are heavily JavaScript-rendered. We run full Playwright browser sessions with lazy-load triggering to capture data headless clients miss.
eCommerce sites change their DOM structure frequently. Our selector strategy uses multiple fallback chains per field so a layout change doesn't break your data pipeline overnight.
For large SKU catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, and coverage drops — responding before you notice.
Travel gear brands and retailers monitor eBags pricing and promotional windows to optimise their own pricing strategies.
Manufacturers track feature sets like TSA locks, weight, and materials across competitor lines to inform product development.
Analysts track category expansion and brand representation within the luggage sector to identify market trends.
Brands audit retail pricing to ensure compliance with Minimum Advertised Price agreements across distribution channels.
Product teams mine review corpora to identify common failure points in luggage hardware like zippers and wheels.
Retailers monitor stock depth and colour variant availability to forecast demand and manage their own procurement.
"eBags holds the definitive dataset for travel gear specifications, dimensions, and brand pricing — but none of it is queryable unless you build the pipeline."
Most teams underestimate the investment required: reliable eBags scraping requires residential proxies, full JavaScript rendering, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis — not the infrastructure.
Everything supported by our ebags.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, 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 US 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 ebags.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from eBags is generally permissible under applicable law. DataFlirt targets only public, non-authenticated product, pricing, and review data. We do not extract personal data or circumvent authentication walls.
We use full Playwright browser sessions to render JavaScript and capture the exact prices displayed to users, including promotional discounts and colour-specific pricing variations.
Yes. We target the specification tables on product detail pages to extract linear dimensions, weight, and volume capacities, normalising the data into structured fields.
Pipelines can be configured for daily or weekly refreshes depending on your requirements. Real-time streaming is available for targeted SKU lists.
Yes. We paginate through all review pages to extract star ratings, full text, helpful votes, and verified buyer status.
Absolutely. We provide a sample run of up to 500 SKUs as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off catalogue dump or a continuous price-monitoring feed across thousands of SKUs — we scope, build, and operate the pipeline. Tell us what you need.
