We extract C2C listings, price drops, seller profiles, and category trends from Carousell. 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 Listings objects from carousell.com. All fields typed and schema-versioned.
"listing_id": "128492018", "title": "Vintage Levi's 90s Denim Jacket", "price": 85.0, "currency": "SGD", "condition": "Used", "category": "Men's Fashion", "likes_count": 42, "seller_username": "vintage_sg_picks"
| # | listing_id | title | price | currency | condition | category |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Status objects from carousell.com. All fields typed and schema-versioned.
"listing_id": "128492018", "price": 85.0, "is_sold": false, "carousell_protection": true, "bump_status": false, "spotlight_status": true, "delivery_options": "['Mailing & Delivery', 'Meet-up']"
| # | listing_id | price | original_price | is_sold | carousell_protection | bump_status |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Seller Profiles objects from carousell.com. All fields typed and schema-versioned.
"username": "vintage_sg_picks", "join_date": "2018-04-12", "verification_status": "['Email', 'Mobile', 'Singpass']", "followers_count": 1204, "average_rating": 4.9, "reviews_count": 342, "response_rate": "98%"
| # | username | join_date | verification_status | followers_count | following_count | average_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from carousell.com. All fields typed and schema-versioned.
"review_id": "REV-938471", "listing_id": "119283746", "reviewer_username": "hypebeast_buyer", "rating": 5, "comment": "Fast deal and item exactly as described. Recommended seller.", "date": "2026-02-14", "transaction_type": "Meet-up"
| # | review_id | listing_id | reviewer_username | rating | comment | date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from carousell.com. All fields typed and schema-versioned.
"keyword": "nike dunk low", "rank_position": 3, "listing_id": "129384756", "is_promoted": true, "title": "Nike Dunk Low Panda US 9", "price": 180.0, "seller_username": "sneakerhead_sg", "scraped_at": "2026-05-12T08:14:00Z"
| # | keyword | rank_position | listing_id | title | price | is_promoted |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Carousell scraper navigates SPA architecture and aggressive bot protection to deliver structured data on listings, sellers, and pricing trends across Southeast Asia.
Capture title, description, condition grading, category taxonomy, image URLs, and posted timestamps for any apparel or general listing.
Track current price, currency, sold status, and Carousell Protection eligibility across thousands of target items.
Extract verification levels, join dates, follower counts, average ratings, and response rates to audit seller reliability.
Paginate through seller feedback to capture raw review text, star ratings, and transaction types.
Monitor SERP positions for specific keywords, capturing both organic results and paid placements.
Identify listings utilising Bumps or Spotlights to understand seller marketing behaviour.
Extract localised data from Carousell Singapore, Malaysia, Hong Kong, Taiwan, and the Philippines.
Capture available shipping methods and specific meetup locations tied to individual listings.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Brief in. Clean data out.
Provide target categories, search keywords, or specific seller profiles. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and Cloudflare bypass mechanisms for carousell.com.
Schema validation, null-rate checks, and data normalisation rules are applied before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Carousell relies heavily on modern SPA frameworks and strict rate limiting. Here is how we maintain stable extraction.
Carousell employs strict rate limits and Cloudflare protection. Our infrastructure routes requests through ISP-grade residential proxies in the target region, maintaining valid TLS fingerprints and cookie sessions to prevent blocks.
Carousell is a heavily JavaScript-rendered single-page application. We utilise Playwright to execute the necessary scripts, ensuring all dynamic content, including lazy-loaded images and nested reviews, is fully hydrated before extraction.
Where possible, our crawlers bypass brittle DOM parsing by intercepting Carousell's internal GraphQL API responses. This provides cleaner, strictly typed data and significantly improves schema stability against frontend UI updates.
For active price monitoring, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute costs and downstream processing load for your engineering team.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, regional blocking anomalies, and schema drift, responding immediately to maintain our contractual SLA.
Apparel brands and sneaker platforms track secondary market valuations to optimise their own pricing strategies.
Marketplaces monitor Carousell's category volume, seller liquidity, and average transaction values to benchmark growth.
Luxury brands audit listings for trademark infringement and counterfeit goods, identifying suspicious seller clusters.
Professional resellers identify price discrepancies for identical items across Carousell SG, MY, and TW.
Fashion analysts track search volume proxies and listing velocity for specific brands to predict upcoming consumer trends.
Hedge funds analyse C2C listing volumes and sell-through rates as macro indicators for regional consumer spending.
"Carousell holds the definitive pulse on Southeast Asia's secondhand economy, but extracting structured C2C data requires bypassing aggressive bot protection."
Most teams underestimate the investment required: reliable Carousell scraping requires residential proxies, full JavaScript rendering for SPA hydration, Cloudflare bypass, and daily schema maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our carousell.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 SPA hydration. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across target Asian regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About carousell.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law. DataFlirt targets only public, non-authenticated listing, pricing, and seller profile data. We do not extract personal data like private messages or circumvent authentication walls.
We use regional residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and automated Cloudflare solvers. We monitor for 403/CAPTCHA rate spikes in real time and trigger pool rotation automatically.
We support Carousell Singapore, Malaysia, Hong Kong, Taiwan, Philippines, and Indonesia, capturing localised pricing and category structures.
Real-time streaming pipelines achieve sub-60-minute latency for target search keywords. Broad category refreshes at daily cadence complete within a 4-8 hour window depending on scale.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per listing ID for price changes and sold status.
Our smallest packages start at a defined keyword or category set (typically 10,000-50,000 listings) with weekly delivery. For larger extractions, we price based on volume and frequency.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off category dump or a continuous price-monitoring feed across 500K listings, we scope, build, and operate the pipeline. Tell us what you need.
