We extract apparel listings, dynamic pricing, brand intelligence, inventory levels, and customer reviews from Zalora. 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 Listings objects from zalora.com. All fields typed and schema-versioned.
"sku": "ZA294AA12BCD", "title": "Classic Cotton Crew Neck T-Shirt", "brand": "Mango", "category": "Clothing", "gender": "Women", "price": 1299.0, "currency": "PHP", "colour": "Navy Blue"
| # | sku | title | brand | category | sub_category | gender |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Promos objects from zalora.com. All fields typed and schema-versioned.
"sku": "ZA294AA12BCD", "current_price": 1299.0, "original_price": 1999.0, "discount_pct": 35, "promo_code_eligible": true, "promo_details": "EXTRA 15% OFF AT CHECKOUT", "price_timestamp": "2026-05-12T10:15:00Z"
| # | sku | current_price | original_price | discount_pct | promo_code_eligible | promo_details |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Inventory & Sizing objects from zalora.com. All fields typed and schema-versioned.
"sku": "ZA294AA12BCD", "variant_id": "VAR-98234", "size": "M", "stock_status": "In Stock", "low_stock_warning": false, "ships_from": "Zalora Warehouse", "scraped_at": "2026-05-12T10:15:02Z"
| # | sku | variant_id | size_system | size | stock_status | stock_quantity |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from zalora.com. All fields typed and schema-versioned.
"review_id": "REV-847291", "sku": "ZA294AA12BCD", "star_rating": 4, "verified_buyer": true, "review_title": "Great fit and material", "fit_feedback": "True to size", "review_date": "2026-04-20"
| # | review_id | sku | reviewer_name | verified_buyer | star_rating | review_title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Brand Intelligence objects from zalora.com. All fields typed and schema-versioned.
"brand_id": "BRD-MANGO", "brand_name": "Mango", "total_products": 4281, "average_discount": 22.5, "new_arrivals_count": 145, "scraped_at": "2026-05-12T10:20:00Z"
| # | brand_id | brand_name | brand_url | total_products | category_spread | average_discount |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Zalora scraper handles every layer of the platform: product catalogues, dynamic pricing, sizing matrices, brand intelligence, and the review corpus : with JavaScript rendering, session management, and anti-bot circumvention built in.
Title, material composition, care instructions, colour variants, and every metadata field Zalora surfaces : scraped at SKU level with parent-child variant mapping.
Capture current price, original price, discount percentages, and promo code eligibility : timestamped per crawl.
Extract available sizes, stock status, and low-stock warnings across all active variants for precise inventory intelligence.
Full review text, star ratings, verified buyer flags, and specific fit/quality feedback : paginated across all review pages.
Track total product counts, category distribution, and new arrival velocity for any brand listed on Zalora.
Track organic position for any keyword or category filter : with exact pagination handling.
zalora.sg, zalora.com.my, zalora.ph, zalora.co.id, zalora.com.hk, and zalora.tw : all from a unified schema.
Monitor flash sale windows, limited-time discounts, and site-wide promo events : useful for repricing and competitor alerting.
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 SKU lists, category URLs, brand pages, or search terms. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for zalora.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.
Zalora relies on dynamic rendering and regional blocking. Here is how we stay resilient : and why teams choose managed infrastructure over DIY.
Zalora monitors request rates and blocks datacenter IPs. Our crawlers use residential ISP proxies localised to the target region (SG, MY, PH, etc.) with realistic browser fingerprints and full cookie session management.
Zalora product pages load sizing matrices and stock alerts dynamically. We run full Playwright browser sessions with JavaScript execution to capture inventory depth that headless HTTP clients miss entirely.
Fashion retail sites update layouts frequently. Our selector strategy uses multiple fallback chains per field : CSS selectors, XPath, and JSON state extraction : so a frontend change does not break your data pipeline.
For large brand 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.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops : and respond before you notice.
Fashion brands and retailers monitor pricing, discount velocity, and promo codes to optimise their own pricing strategies.
Brands track their representation, pricing consistency, and stock levels across Zalora regional storefronts.
Analysts track new arrivals, category growth, and colour popularity to identify emerging fashion trends.
ML teams use Zalora apparel datasets to train visual search engines, recommendation algorithms, and sizing models.
Supply chain teams correlate stock-out rates and sizing demand to improve procurement and manufacturing models.
Retailers track competitor brand catalogues, discount depth, and review sentiment to evaluate market positioning.
"Zalora represents the pulse of Southeast Asian fashion retail. Extracting its catalogue data provides unparalleled visibility into regional apparel trends and pricing dynamics."
Most teams underestimate the investment required: reliable Zalora scraping requires regional residential proxies, full JavaScript rendering for inventory states, 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 zalora.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 Southeast Asia. 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 zalora.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Zalora is generally permissible under applicable laws. DataFlirt targets only public, non-authenticated product, pricing, inventory, and review data. We do not extract personal data, circumvent authentication walls, or violate GDPR/PDPA. Clients should review Zalora ToS and consult legal counsel for specific use cases.
We use residential ISP proxies localised to the specific Zalora region (e.g., Singapore IPs for zalora.sg, Philippine IPs for zalora.ph). This ensures accurate regional pricing and prevents geo-blocking.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined SKU set. Full brand catalogue refreshes at daily cadence complete within a 6-12 hour window depending on size.
Yes. Our pipeline renders the dynamic sizing matrix for every product, capturing which specific sizes are in stock, out of stock, or flagged with low-stock warnings.
Our smallest packages start at a defined brand list or category subset (typically 5,000-20,000 SKUs) with weekly delivery. For larger catalogues or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 SKUs or 50 category pages as part of the pre-engagement scoping process : so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off apparel catalogue dump or a continuous price-monitoring feed across 500K SKUs : we scope, build, and operate the pipeline. Tell us what you need.
