We extract flash sale events, boutique pricing, inventory levels, and apparel specifications from Zulily. Delivered as clean JSON, CSV, or Parquet to S3 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 Flash Sale Events objects from zulily.com. All fields typed and schema-versioned.
"event_id": "evt_948172", "brand_name": "Matilda Jane", "event_title": "Matilda Jane Clothing: Up to 60% Off", "start_time": "2023-10-14T06:00:00Z", "end_time": "2023-10-17T06:00:00Z", "product_count": 142, "status": "active"
| # | event_id | brand_name | event_title | start_time | end_time | banner_image_url |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Product Listings objects from zulily.com. All fields typed and schema-versioned.
"product_id": "prd_883192", "title": "Navy Floral Tunic", "brand": "Matilda Jane", "price": 24.99, "msrp": 48.0, "discount_pct": 47, "colour": "Navy", "material": "95% Cotton, 5% Spandex"
| # | product_id | event_id | title | brand | category | sub_category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Inventory objects from zulily.com. All fields typed and schema-versioned.
"product_id": "prd_883192", "current_price": 24.99, "original_price": 48.0, "currency": "USD", "in_stock": true, "waitlist_available": false, "shipping_estimate": "10-14 days", "price_timestamp": "2023-10-15T08:30:00Z"
| # | product_id | current_price | original_price | currency | in_stock | stock_level |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Apparel Specifications objects from zulily.com. All fields typed and schema-versioned.
"product_id": "prd_883192", "fit_type": "Relaxed", "fabric_composition": "95% Cotton, 5% Spandex", "pattern": "Floral", "neckline": "Crew", "sleeve_length": "Long Sleeve", "care_instructions": "Machine wash cold"
| # | product_id | size_chart_url | fit_type | model_measurements | fabric_composition | pattern |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand Data objects from zulily.com. All fields typed and schema-versioned.
"brand_id": "brd_441", "brand_name": "Matilda Jane", "active_events": 2, "average_discount": 45.5, "category_focus": "Girls Apparel", "scraped_at": "2023-10-15T08:30:00Z"
| # | brand_id | brand_name | active_events | past_events | average_discount | brand_description |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Zulily scraper handles every layer of the platform: flash sale events, boutique pricing, variant matrices, and waitlist status, with JavaScript rendering and session management built in.
Track event start and end times, banner images, and total product counts per boutique event.
Capture boutique pricing, MSRP, and calculated discount percentages timestamped per crawl.
Map colour and size combinations accurately across apparel listings to build complete product matrices.
Monitor stock depletion and waitlist availability indicators for high-demand boutique items.
Group products by daily deal events and extract brand-level metadata and historical event frequency.
Extract high-resolution product image URLs and brand banner assets for visual analysis.
Structured extraction of sizing tables, fit descriptions, and fabric composition data.
Crawl through women's, kids, home, and beauty categories systematically.
Bypass perimeter defenses and aggressive login prompts using residential proxies and realistic fingerprints.
Hourly or daily syncs matching Zulily's morning deal launch cadence for maximum data freshness.
Brief in. Clean data out.
Provide target categories, brand names, or event URLs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management for zulily.com.
Schema validation, null-rate checks, price-outlier detection, and sample data before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Zulily's ephemeral catalogue and aggressive login walls require specialised infrastructure. Here is how we maintain stable extraction.
Zulily launches thousands of products simultaneously at 6 AM PST. We scale our Kubernetes workers dynamically to capture the entire catalogue within minutes of launch before high-demand items sell out.
Zulily frequently gates browsing behind a login wall. We manage authenticated cookie pools and rotate sessions automatically to maintain uninterrupted access to public catalogue data.
Apparel listings feature complex matrices of sizes and colours. Our parsers map every possible combination, recording specific stock status and pricing for each SKUs variant.
Product images are heavily lazy-loaded. We use Playwright to simulate scroll behaviour and network idle states, ensuring all high-resolution image URLs are captured before extraction.
We route requests through US-based residential ISP proxies to avoid datacenter IP bans, maintaining high success rates even during aggressive burst crawls.
Retailers monitor Zulily's boutique pricing and discount depths to adjust their own promotional strategies.
Brands track flash sales to ensure third-party sellers and liquidators adhere to Minimum Advertised Price agreements.
Analysts monitor waitlist metrics and stock depletion rates to gauge consumer demand for specific apparel categories.
E-commerce strategists study event duration, product mix, and timing to optimise their own daily deal structures.
Firms track emerging boutique brands and category saturation to identify new wholesale opportunities.
Machine learning teams use structured apparel attributes and high-resolution images to train visual search and recommendation engines.
"Zulily's flash sale model creates a highly volatile catalogue where thousands of products vanish daily. Capturing this requires precision timing."
Extracting data from ephemeral daily deals requires infrastructure that can burst at specific hours. We handle the residential proxies, JavaScript rendering, and session management needed to bypass login walls and extract complete apparel catalogues before the event timer hits zero.
Everything supported by our zulily.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 deduplication. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for Zulily's dynamic frontend.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions to maintain authenticated states.
Pipelines run on Kubernetes for burst scaling during 6 AM deal launches. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About zulily.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zulily is generally permissible under applicable law. DataFlirt targets only public product, pricing, and event data. We do not extract personal data or user purchase histories. Clients should review Zulily's ToS and consult legal counsel for specific use cases.
We manage authenticated cookie pools and use residential ISP proxies with realistic browser fingerprints. This allows us to bypass aggressive login prompts and access the public catalogue reliably.
Yes. We configure burst-scaling infrastructure to trigger extractions precisely at Zulily's daily launch times, ensuring you capture inventory and pricing before items sell out.
Yes. Our parsers map the complete variant matrix for every apparel item, capturing specific pricing, stock status, and waitlist availability for each size and colour combination.
Yes. We extract the waitlist availability flags on sold-out items, providing valuable signals for product demand and inventory analysis.
We deliver data in JSON, CSV, XLS, and Parquet formats. We can push directly to AWS S3, Snowflake, or trigger Webhooks for real-time integration.
Absolutely. We provide a sample run of up to 500 products 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 apparel catalogue dump or a continuous daily deal monitoring feed, we scope, build, and operate the pipeline. Tell us what you need.
