DEV Community: Sergii Klius

How to Scrape Zalando and ASOS: European Fashion Retail Data

Sergii Klius — Wed, 04 Nov 2026 14:10:24 +0000

Key Takeaways

Running a Zalando and ASOS scraper requires handling an advanced anti-bot system and handling dynamic page content.

Best practices include ensuring session consistency, reverse-engineering internal and mobile APIs, and selecting high-quality proxies.

With complex targets like retail websites, hiring a professional scraping company can help get results faster.

Your ASOS scraper may work one day and return a 403 error the next. Big fashion retailers invest heavily in anti-bot protections and update them on a regular basis. If you're building a data-driven product or analytics system, prolonged pipeline downtime can hinder the accuracy of your insights.

In this article, we explain the challenges your retail scraper faces and show how to overcome them when running large-scale collection.

The Main Challenges of Running a Zalando Scraper

Automated data collection from platforms like ASOS and Zalando is often difficult to maintain. Below is a list of the most common issues to help you assess the risks before you start scraping and troubleshoot when something goes wrong.

1. Legal challenges of scraping in Europe

Although collecting publicly available data is generally legal across the world, the particular laws differ depending on use cases, data types, and collection methods. While non-EU European countries often have less strict approaches, the European Union is one of the most regulated markets for web scraping.

Before building a Zalando scraper that covers Germany and other EU states, we recommend consulting a lawyer or doing your own research. Here's a brief overview of regulations to consider.

Personal data. Names, face photos, and phone numbers are protected by the GDPR. Collecting them requires explicit consent or a documented legitimate interest. When scraping reviews, make sure you do not gather or store any personal or identifiable reviewer details.
Sources protected by a website's Terms of Service. The EU courts view ToS as a form of a legally binding contract. In Ryanair v. PR Aviation, the price comparison platform was ruled to stop scraping Ryanair's website in violation of its ToS. Note that both ASOS and Zalando prohibit "screen scraping" in their Terms & Conditions.

2. Akamai Bot Manager

If you're clear legally, the next obstacle is websites' anti-bot protections. Both ASOS and Zalando use Akamai Bot Manager, a system that passes each request through an edge server network where detection happens. It assesses traffic using a set of parameters:

IP and ASN reputation. Akamai owns a dedicated database and records the history of each address's activities across all websites that use Akamai's services.
TLS and HTTP fingerprints. All the session's signals need to be consistent.
Device and browser telemetry. The system uses JavaScript Challenges to assess additional signals.
Behavioral signals. Akamai uses AI to analyze mouse movements and typing patterns and flag artificial sessions.

The system is maintained and regularly updated. Even if you've managed to bypass it once, it doesn't guarantee continuous uptime.

3. Dynamic content

Zalando uses client-side JavaScript rendering for all the product data. That means a page's HTML is empty before a browser loads the dynamic content. If your scraper relies on HTTP requests, it won't return any data.

For ASOS, dynamic rendering is partial. As a result, a BeautifulSoup-based scraper can appear functioning until you try to use the collected data. You can get the product title and image but miss the price, as happened in one Stack Overflow question. For reliable results, use JS rendering or pull data from another source.

Best Practices for Building an ASOS Scraper

Following the right practices from the start can help keep your data pipeline running as your needs grow. Below are a few essential steps to follow, along with additional alternative ways to get fashion data at scale.

1. Select the right proxies

With advanced protection systems like Akamai, cheap datacenter proxies are blocked before a scraper can load a single page. The same can be true for residential or mobile IPs that have already been used to scrape Akamai-protected websites without taking the right precautions.

Choose a reputable proxy provider, read reviews, and try the addresses on a small run before committing.

2. Manage geo-specific signals

Zalando has separate versions for different European countries. When accessing each one, make sure your IP and browser signals match the particular location. Foreign proxies are not blocked automatically, but inconsistent geographic and language markers within a single session can trigger a CAPTCHA or make the system watch you more closely.

3. Reverse engineer internal APIs

Fashion retailers host millions of items. If your analysis needs to cover a large portion of the data, scraping it page-by-page will take a lot of time and resources. In addition, large scale makes it harder to imitate human sessions. If you send too many requests without adding natural pauses and rotating user agents, you'll get blocked. Finding an API is a popular alternative.

Retail websites don't have official public APIs third-party developers could use, but they still use API connections for internal data transfer. Finding the endpoints can help pull data without crawling. Here's a quick guide:

Open your browser's Developer Tools on a page containing the data you need.
Inspect network requests and identify the relevant one.
Copy it as cURL to reproduce the request.
Test it outside the browser, for example, in a terminal.
Determine which parameters are required for the request to work consistently by removing them one by one.
Recreate the request in your scraper.

This is not a universal solution, as the internal API are also often protected by Akamai or other tools. In addition, they can change at any moment, requiring continuous monitoring and maintenance.

4. Try scraping mobile apps

When building an ASOS scraper, app version of the platform can offer another internal API to work with. A reverse-engineered mobile API can deliver clean, structured JSON that is easier to integrate than raw scraped HTML. As protections evolve, one target can become easier than another. Run tests before investing in a full-scale pipeline.

5. Consider professional services

In-house development doesn't make your ASOS scraper free. Even if you have a team necessary to build a reliable pipeline, the costs add up. The scraping market offers ready-to-use solutions as well as custom services that can deliver your scraper fast. The best option for you depends on your use case.

ASOS Scraper Review: Pre-Built Tool vs. Custom Web Scraping Services

Since retail is a common target for data collection, finding a pre-made Zalando scraper is not hard. However, self-serve tools don't fit all projects. Below is a brief overview of their pros and cons compared to custom services.

Scraping option	Benefits	Downsides
Custom services	Built around your needs; diverse integration options; transparent costs per-project; dedicated maintenance team; zero tech skills required from you	Delayed start due to development; larger upfront investment
Pre-built tool	Same-day data delivery; low costs for small projects; maintenance handled by the provider	Built for generic use cases; limited customization; unpredictable price at scale

One of the main differences is how much work your team is willing to handle. Even if you find a no-code solution that claims to work straight out of the box, you still have to set it up and process the output. With custom services, you can choose any level of involvement: from data file delivery to end-to-end development where every stage is handled for you.

Before buying an ASOS scraper, review the integration and scalability options. We recommend taking a long-term approach. Pre-built tools work for one-time competitor assessment or small scheduled scraping. If the data needs to be part of your workflow or product, custom development can offer more.

This article is for informational and educational purposes only. Before collecting any data from ASOS, Zalando, or any other platform, consult a qualified legal professional to ensure compliance with the platform's terms of service.

How to Scrape Shein and Temu: Fast Fashion Marketplace Data

Sergii Klius — Wed, 04 Nov 2026 13:29:49 +0000

Key Takeaways

Shein and Temu can be hard to scrape because of bot detection, IP blocks, and dynamic content.

A reliable Shein scraper needs to use high-quality residential or mobile proxies and imitate human behavior by maintaining a stable, internally consistent browser session.

Most scraping APIs struggle to scrape Shein, making custom services the most reliable option.

Keeping up with fast fashion requires a reliable data collection pipeline. If your Shein scraper stays down for days, you miss more market shifts than in any other industry. Keeping it running can be challenging as layouts shift and anti-bot measures evolve.

In this article, we break down the main problems of scraping Shein and Temu and explain how to overcome them fast.

The Main Challenges of Running a Shein and Temu Scraper

Like most popular marketplaces, Shein and Temu prohibit automated data collection in their terms of service and employ various protective measures to restrict scrapers. Both platforms use Cloudflare, one of the top protection services on the market. That's where most of the challenges come from: IPs with suspicious activity are blocked before the data is delivered. Here's a breakdown of the problems you may encounter when scraping these platforms.

1. IP blacklisting

Cloudflare checks IP reputation against its own rating system, analyzing your session's characteristics and the previous history of the IP across Cloudflare's network. If a Temu scraper API runs on a datacenter proxy or an overused residential IP, it gets blocked immediately. A reliable solution requires high-quality mobile or residential proxies. Choosing a reputable proxy network helps avoid this.

2. Rate limiting

Large-scale data collection requires more requests per second than any human session includes. If you go too fast without rotating user agents, the system blocks you. An off-the-shelf Shein scraper API will have built-in limits; for your own crawler, you'll need to manage the rates to keep them realistic.

3. CAPTCHA

Any suspicious activity on your part may cause the system to demand an additional check. Note that today's CAPTCHA assesses your overall behavior and cookies, not just your solution to a small puzzle. You can use AI tools or manage the issues manually. However, the best approach is to avoid triggering CAPTCHA rather than trying to solve it.

4. JS rendering

Though it's not used specifically to limit scrapers, dynamic content is still a challenge for solutions that parse static HTML. Unless your pipeline includes a tool that loads JavaScript for further parsing, it will return empty fields.

Best Practices for a Shein Scraper

Despite the challenges, building a reliable Shein data pipeline is possible. Here are a few measures to take before you start scraping to minimize breaks and delays in data delivery.

1. Check for available APIs

Both Shein and Temu have official APIs that can deliver platform information automatically. However, API access is often restricted to a selected list of partners. Becoming one of them requires applying as a developer or researcher working on one of the approved use cases. Below is the detailed breakdown.

Official API type	Shein	Temu
Research API	Not available	Available for researches who went through the application process; can't be used for personal or commercial purposes
Seller API	Available for uploading products	Add Products API
Developer API	For registered developers building apps that enhance the seller experience	For sellers and partner developers that build automation for sellers; available via the Partner Platform
Public API for data collection	Not available	Not available

If your project fits one of the descriptions (for example, you're building an inventory management tool for Shein sellers), using an official API is the easiest and the most ethical way. If you don't want to go through each platform's application and review process, you're left with unofficial tools like a Temu scraper API.

Another option is building or commissioning a custom scraper. Either way, the solution has to implement the best practices below.

2. Imitate human behavior

This is the main principle in scraping that allows you to mimic a real user session and comply with website rules. Here are a few elements to cover:

use Bézier curves to produce natural cursor movements;
vary cursor movement speed and make it slow down near the target;
add micro-corrections;
scroll in small, uneven increments, adding pauses that mimic reading;
add randomized delays between clicks and requests;
visit intermediate pages before the target page;
add variable intervals between keystrokes;
add typos followed by corrections.

3. Use headless browsers

Tools like Selenium or Puppeteer help solve multiple scraping problems at once, including:

Scraping dynamic content. Browsers load JavaScript and allow you to parse the content not rendered by the server.
Handling infinite scroll and lazy-loaded content that is not available without a scroll event.
Closing automated popups that block data view. Shein and Temu often spam users with special offers and ad banners.
Imitating a human session to avoid bot detection.

However, simply running Puppeteer is often not enough; you'll have to manage the session step-by-step.

4. Maintain a stable session

Ensure your session is internally consistent. That means keeping the same cookies, browser fingerprint, IP address, and account across a sequence of requests. Avoid switching IPs mid-flow, as it can put your account at risk. Make sure your geo-specific browser settings match your proxies.

Being mindful of geo markers has another benefit: you get a more accurate output. Temu prices change depending on the session, location, and other user characteristics. Rotating user agents with detailed imitations allows you to compare different locations, get insights into the marketplaces' pricing strategies, and get only the data that fits your research needs.

Use Cases for Integrating a Shein and Temu Scraper

The specific configurations of your marketplace parser depend on your use case. Shein and Temu provide numerous signals about fashion industry trends in terms of both supply and demand. Below are a few examples of the intelligence systems their data can feed.

1. Market research

Automated monitoring is the only practical way to stay on top of the fast-changing fashion market. Scraping can help you track changing prices automatically, integrate the data into an analytics dashboard or set up notifications for meaningful changes.

Large-scale data collection allows analysts to spot trends and seasonal demand fluctuations. Comparative research helps highlight regional preferences and demand patterns and see how prices and offerings differ across Temu, Amazon, or local marketplaces.

2. Sentiment analysis

Analyzing best-selling and highly rated items gives insights into current demand and prevalent consumer preferences. Scraping reviews provides even more insights into common quality problems and unmet customer needs. Depending on your purpose, you can adjust your scraper to collect feedback with a specific rating or keyword mentioned.

3. Fashion trend prediction

Shein demonstrates changing microtrends better than any other platform. A scraping pipeline can track new items as they emerge and gain popularity across markets. You can also notice when a trend starts to die down and is no longer worth investing in.

4. Counterfeit detection

Shein is full of products inspired by popular designs and unique pieces. When they cross the line between inspiration and outright copying, the platform is willing to take them down. However, IP owners have to identify them themselves and file a complaint.

A scraper can automate the process, pulling product images to compare them with original designs and flag infringements. Full rendering of the dynamic content allows the system to make screenshots that can be used as evidence.

Shein Scraper API vs. Custom Web Scraping Services

The scraping market offers a lot of options that fit different use cases. A tool like an Apify Temu scraper has a few benefits:

same-day data delivery;
lower costs (at least at limited scale);
scrapers maintained by the community.

However, if you're building an analytics tool or another project that relies on scraping long-term, the main point to consider is reliability. Protections make Shein a hard target to scrape. According to Proxyway testing, only 21.88% of requests made with popular scraping APIs were successful. Many pre-built tools couldn't even open the page.

With complex targets like these, some scraper breaks and request failures are normal. Custom development can't guarantee a 100% success rate either. The difference lies in dealing with the problems. A reliable provider handles the retries, monitoring, and maintenance to minimize your downtime. An out-of-the-box tool often leaves you to it.

This article is for informational and educational purposes only. Before collecting any data from Shein, Temu, or any other platform, consult a qualified legal professional to ensure compliance with the platform's terms of service.

How to Scrape Product Hunt Data for Market Research

Sergii Klius — Wed, 04 Nov 2026 13:21:28 +0000

A reliable Product Hunt scraper needs to handle common challenges like bot detection, dynamic content, and frequent source updates. Both pre-built and custom options can meet these needs, but on different scales. For long-term monitoring and seamless data integration, bespoke scrapers show better results.

Product Hunt is a valuable resource to analyze the SaaS market or monitor the investment landscape. Unless you want to spend hours scrolling through the platform, getting insights requires automated access.

This article breaks down how to set up a Product Hunt scraper, what roadmap to follow, and when hiring professionals is the best option.

The Main Challenges of Scraping Product Hunt

Like most business data sources, PH uses specialized measures to restrict scraping. Though most of the data is publicly available without paywalls or logins, large-scale collection triggers protections and faces other challenges.

1. Frequent data updates

Product Hunt's operation relies on fast data changes: leaderboard updates daily, and vote counts grow every minute. One-time extraction doesn't always provide enough information to analyze market trends. To stay updated, you'll need to request data often.

2. Bot detection

Frequent requests overload source servers. In response, most websites limit frequency and block suspicious sessions and IPs. For continuous connection, scraping needs to run slower and combine several separate crawlers that work in parallel for faster results. Even when you manage your rates, automated traffic patterns are flagged unless you take extra steps to imitate human behavior.

3. JavaScript-rendered content

PH is based on React. All product listings, vote counts, and comment data load dynamically through client-side JavaScript. A Product Hunt profile scraper that sends standard HTTP requests gets empty fields and requires headless browsers to render the content first. They, however, have their own challenges.

4. Headless browser detection

Detection systems check for specific signals headless browsers like Playwright and Puppeteer leave in the browser environment. Stealth patches can help, but require constant monitoring and updating as source protections evolve.

5. Frequent layout updates

Product Hunt updates its frontend regularly, changing structure, tweaking design, and adding new features. Selector-based or API-targeting scrapers break every time, adding another point to your maintenance checklist.

Running a Product Hunt Profile Scraper: a Step-by-Step Guide

Whether you need to collect data from PH profiles, product details, or forum threads, there are ways to do it efficiently. We'll walk you through the main stages and the things to pay attention to at each one.

1. Plan your pipeline

Your target data use defines the whole collection process. One-time extraction for market assessment requires a scraper that works for a day. If you need regular updates, plan for maintenance and reliable delivery infrastructure.

Choose the right data formats that fit your storage or software and adapt extraction logic to your analysis. If raw extracted data skews your results, plan for a transformation layer. For large-scale analytic projects where you add G2 scraping or other relevant sources, make sure you unify and normalize the data.

2. Select your toolkit

PH offers an official API that allows you to pull public data through GraphQL requests. It is the simplest method to get the platform's data, so we recommend checking it out first. However, it has limitations:

the API is subject to rate limits (up to 6250 complexity points or 450 requests every 15 minutes);
the API doesn't deliver Maker and other user data to protect personal information;
PH prohibits using the API for commercial purposes without getting their permission.

If these are dealbreakers for you, a scraper can be a better option. You can build it in-house, buy a ready-made solution like an Apify Product Hunt scraper, or commission a custom pipeline.

3. Extract and structure Product Hunt data

Render the dynamic content and pull the data points from the specific fields necessary for your use case. Before sending the data into storage, go through the following steps:

normalize records;
validate data formats;
deduplicate;
timestamp each record;
flag incomplete fields.

These actions are necessary to prevent downstream errors. Once the data is cleaned and checked, it's ready for the next stage.

4. Integrate the output

You can load data files or use APIs for a direct database feed. As long as your field names and data types are mapped to the end system's schema, no post-delivery reformatting is required. Custom scrapers allow businesses to get better PH-informed insights than the limited output of popular LLMs.

5. Monitor and maintain

Even top-quality scrapers require ongoing maintenance. Moreover, you need a monitoring layer to notice pipeline failures before they cause data gaps. Here's what to track:

record count per run;
field completeness;
session and authentication status;
proxy success rate;
GraphQL endpoint availability.

The process can be handled by a scraping company either fully or partially. It depends on the engagement model you choose.

Pre-built Product Hunt Scraper vs. Custom Services

The better option for you depends on your specific use case. Here is a comparison using an Apify Product Hunt scraper Actor as an example.

	Ready-made	Custom-built
Example	Apify Product Hunt scraper	DataOx custom services
Scraper configuration	You enter URLs, select dates, level of detail, timeouts, and choose proxy settings	Built to your specs by DataOx
Data integration	Apify API	Integration with your system, app, or SaaS handled by DataOx; custom API development
Health monitoring	Failed request notifications included; data quality checks handled by you (missing fields, incomplete data, etc)	Validation and monitoring handled by DataOx
Pipeline maintenance	Maintained by Apify community members	Handled ASAP by DataOx

The comparison shows that a pre-built scraper works for one-time same-day delivery. For long term-projects, clients have to handle part of the monitoring and maintenance themselves. It can be a challenge for some teams.

This article is for informational and educational purposes only. Before collecting any data from Product Hunt or any other platform, consult a qualified legal professional to ensure compliance with the platform's terms of service.

Scrape Walmart: Proven Tools for Price Tracking

Sergii Klius — Mon, 26 Oct 2026 17:36:48 +0000

Key Takeaways

The Walmart API has real limits for competitive monitoring. Rate limits, throttling, and a narrower field set make it unsuitable for large-scale competitor tracking.
Off-the-shelf scrapers (ScrapeHero, ParseHub, ProWebScraper) cover basic extraction well, but Walmart's scale with frequent layout changes, dynamic pagination, and asynchronous data loading implies the usage of custom-built solutions.
Walmart scrapers can extract such details: product information, pricing, seller and fulfillment, reviews and ratings, availability and logistics, search and visibility.

Third-party sellers now supply 95% of the 420+ million products listed on Walmart Marketplace. Therefore, most of what shows up in a Walmart search result today is competitive inventory you're bidding against. With 430+ million monthly visits landing on that inventory, pricing decisions made without awareness of statistics are deliberately losing. It is possible to scrape Walmart with its official APIs, but rate limits, throttling, and restrictions on how Walmart data may be used can make large-scale data collection and scaling more challenging.

In this article, we will show you how to use the best Walmart scrapers, and explain what you should do if off-the-shelf tools can't cover the needs of your business.

Ecommerce Product Data Scraping: Use Cases

By monitoring other e-commerce websites you can analyze their partners and products understand their customers' behavior and demands, make price comparisons and optimization as well as collect feedback about new products.

Use Case	What Gets Scraped	Business Outcome
Competitive price monitoring	Prices across product categories, price history	Adjust pricing in real time to stay competitive without manual checks
Product catalog benchmarking	Titles, descriptions, images, category placement	Identify catalog gaps and optimize listing quality against top performers
Review and sentiment analysis	Star ratings, review text, review volume	Spot common product complaints and compliments
Seller and marketplace research	Third-party seller counts, seller ratings, fulfillment type	Evaluate market saturation before entering a category
Inventory and availability tracking	Stock status, shipping estimates, out-of-stock frequency	Anticipate changes in demand and adjust supply chain planning
Advertising and placement research	Sponsored product positions, search ranking patterns	Understand what affects the visibility on Walmart's search results

How to Get Walmart Scraper: Tools and Custom Options

You've probably heard about different ways to get data from Walmart, and you might think what is the best Walmart data scraper. Actually, there are a lot of good options, and some of them offer free trials.

But if you have specific requirements, you can subscribe to web scraping services and build your own scraper or order a customized Walmart scraper like Walmart inventory scraper or Walmart price scraper and extract data according to your requirements. Let's see what kind of data you can scrape from Walmart:

Product details

Product title and full description
Brand and manufacturer
SKU / item ID / Walmart-specific product ID
Category and subcategory breadcrumb
Product images
Variant options (size, color, pack quantity) and their individual prices
Product specifications and attribute tables (dimensions, materials, etc.)

Pricing

Current price
List price / "was" price (for discount calculation)
Rollback and clearance flags
Price per unit (e.g., price per ounce)
Walmart+ member pricing, where applicable

Seller and fulfillment

Seller name (Walmart itself vs. third-party marketplace seller)
Seller rating and review count
Fulfillment type (Walmart Fulfillment Services vs. seller-fulfilled)
Number of sellers offering the same product

Reviews and ratings

Overall star rating
Total review count
Individual review text, rating, and reviewer name
Verified purchase flag
Review date
Helpful vote counts

Availability and logistics

Stock status (in stock, limited stock, out of stock)
Estimated shipping and delivery dates
Store pickup availability by location
Return policy details shown on the listing page

Search and visibility

Organic search ranking position
Sponsored/ad placement flags
"Best seller" or other badge indicators
Category page ranking

Ecommerce Product Data Scraping: Best Walmart Tools

Web scraping tools are used for many purposes like recruitment data scraping, marketing analysis, financial reports, and in many other industries as well as in e-commerce. Let's review exactly Walmart scrapers.

ScrapeHero

ScrapeHero offers a cloud-based and pre-built scraper, so you are free of downloading any software and pointing out the fields to be extracted. The scraper can be accessed from any browser whenever you want and deliver the required data directly to Dropbox.

ParseHub

ParseHub offers a powerful Walmart web scraper that allows users to choose a product category from and extract data into different formats.

ProWebScraper

ProWebScraper enables automation of the Walmart scraping process and helps to scrape product-related data and images. With ProWebScraper your first 100 pages will be free of charge.

How to Scrape Walmart Data: 3 Simple Steps

As a rule, when you build your own scraper using the equivalent tools, you have to make 3 simple steps:

Create an account at your scraping service provider
Create and set up a scraper according to your requirements
Download your data

Scrape Walmart Reviews Results: DIY vs Outsourcing

If you aren't excited about building your own Walmart scraper and prefer to get only the required data, then your solution is web scraping companies outsourcing. Such companies have huge experience in extracting and delivering billions of data. They have an expert team of web scraping developers and can handle large volumes and varied types of data extraction per the client's request. On top of this, they are aware of all kinds of security and anti-scraping issues and can overcome them easily.

In the case of e-commerce data scraping like Walmart, Amazon, or eBay, it is crucial to have expertise since product changes occur on such portals every day, and handling changes in such considerable amounts of data is a tough challenge. Additionally, web scraping companies can provide you with data delivery, data visualization, AI integration, analytics software development, etc.

Web Scraping for Brand Protection Providers: 5 Best Practices

Sergii Klius — Mon, 26 Oct 2026 17:28:01 +0000

Web scraping for brand protection serves as coverage for a business. Your clients, or your own licensing model, depend on counterfeit listings, unauthorized resellers and copied images. You can find it on Amazon and eBay, but also on the regional marketplaces and niche sites that simple tools can't handle.

Additionally, your lawyers or legal team need to know when the evidence was found, the source, and its content. This article shows how web scraping closes those coverage gaps, and describes five practices that keep the data reliable.

Brand Monitoring Use Cases for Legal and Compliance

Whether you need enforcement for brand clients or protecting your own catalog, the data has to answer these questions:

Where are counterfeit or similar versions of the product sold out?
Who is reselling without authorization, or breaking agreed pricing?
Where are copyrighted images used without a license?
Is there enough documented evidence for a takedown or a licensing claim?

Another set of questions touches on the scraping process and issues with providers:

A client needs regions or marketplaces our current tools don't cover. How fast can we add them?
Our in-house scrapers keep breaking. Is maintaining them still worth it at this volume?
Our last freelancer or agency stopped delivering. Who can continue their work and support existing system?
Our evidence is still assembled from manual screenshots. Can it arrive structured and ready for lawyers?

Here's how different monitoring use cases map to legal and compliance outcomes:

Use Case	What Gets Monitored	Outcome
Trademark misuse detection	Logo and brand name usage across web, marketplaces	Licensing enforcement
Counterfeit product detection	Product listings, images, pricing on marketplaces	Takedown requests, revenue recovery
Unauthorized image/content use	Visual matches across websites	Copyright claims, new licensing revenue
Domain and phishing monitoring	Lookalike domains, fake storefronts	Fraud prevention, brand safety
Franchise/reseller compliance	Partner sites for pricing, messaging compliance	Contract enforcement

How to Build Counterfeit Detection Pipeline with Web Scraping

A counterfeit detection pipeline collects cases from public marketplace pages for a review. It supplies the raw observations that hypothetically need a person's attention. Each stage has clear reasons, the foundation that can be inspected.

Start with the reference set

Before we build a system, the brand supplies its protected products with SKUs, models, variants, approved images, logos, and canonical titles, along with a list of authorized sellers, distributors, and domains. This reference can be updated or changed with time to provide a correct basis for scrapers.

Choose sources by where the brand leaks

Marketplaces, retailer sites, search results, social profiles, and ad libraries leak in different ways. For each, we should define fields and schedule, depending or principle and secondary goals.

Collect the listing with its context

Each cycle of scraping stores the listing fields (title, price, currency, seller, product identifiers, variants) together with the URL, market, language, capture time, and retrieval state. It can also contain screenshots and rendered pages as evidence. This stage involves proxy rotation, browser fingerprinting, and retry logic.

Match against the reference set

Matching combines several cues. When sellers copy, it is most evident through identifiers such as GTINs and model numbers. Normalized text title catches near-duplicates. Image comparison receives listings that reuse a brand's photos or imitate its logo. A listing rarely matches on one cue alone, so the pipeline records which signals matched and which were missing.

Package the case for a reviewer

A candidate for a legal team contains, for example, the matched cues, the missing cues, the screenshot, the price history, and the seller. All of that is custom and can be edited according to your needs. Authorized sellers are excluded or marked. All the records can also be filtered from most to less relevant.

Team's verdict

The final decision on whether a product is fake, authorized, or infringing gets decided by the brand's legal and compliance staff, as well as the solution.

Watch the pipeline itself

The pipeline stores first seen, last seen, and each changed field for a correct analysis. Errors in scrapers' work get marked and timely corrected. All the necessary documentation for a subsequent reasonable examination is inside the pipeline.

Web Scraping Fraud Detection: Best Practices

Fraud detection involves the expertise of a large number of unstable sources. These practices will help you stay efficient, achieve results, and at the same time, save some resources.

Run cheap signals first. Check the domain and certificate first, then fetch page content, then run visual comparison on what remains. This way, accuracy will increase.
Match names in several ways. Try to edit distance on domain names, brand keyword matches, and decoding of Punycode to catch lookalike characters (homograph attacks). Certificate Transparency logs record every publicly trusted certificate, which can be used as an early-warning source for new lookalike domains.
Make a periodical re-check. Fake shops have a tendency to be online for a short time, disappear, and be active again. DataOx recommends tracking known lookalike domains that stopped being active. Store first seen, last seen, and changed fields, as in the pipeline section. It's possible that after a pause, fraud stores can be reopened.
Preserve evidence at the moment of detection. Practice saving the rendered page, the raw HTML, HTTP headers and response codes, a hash, and a trusted timestamp (RFC 3161), ideally in WARC format (ISO 28500). Under EU rules, qualified timestamps adds accuracy (eIDAS Article 41).
Keep scope narrow. Public pages only, no logins, no test purchases, no seller contact. Reviews and profiles can hold personal data.

DataOx Case Studies: Web Scraping for Brand Protection in Action

One of our long-term clients is a European company in a legal industry that use an anti-counterfeiting platform and monitors e-commerce marketplaces for its consumer brand customers. We took control over scraper maintenance, monitoring, proxy rotation, and anti-bot management. The pipeline included data validation, delivery, and integration into the client's system.

The scrapers work on Python, Java, and Playwright. Health monitoring notices silent breaks, an architecture improved with retries and a validation layer isolates failures, while AI-assisted parsing keeps receiving data during fixes.

Over more than three years, the platform grew to 100+ marketplaces, including Amazon, eBay, Zalando, AliExpress, and Temu, with geo-specific scrapers for 15+ EU markets. It now processes over 800,000 records a day at 99.9% pipeline availability. The engineering workload of our client fell by 30%, counterfeit listings are identified twice as fast. With our custom solution, the client can focus on detection and takedown rates.

Scrape Website for Keywords: 4 Reliable Solutions Compared

Sergii Klius — Mon, 26 Oct 2026 17:21:34 +0000

Key Takeaways

Free tools like ParseHub, Octoparse, and WebHarvy handle one-time or small-scale keyword scraping well, but each requires manual setup per project and has no built-in way for recurring schedule.
The selection of focus data fields (ranking position, ad copy, search volume trends, or brand mentions) depends on the team that uses it. It is often the real deciding factor between a free tool and a custom-built scraper.
Scaling keyword tracking across many locations, languages, or hundreds of terms requires ongoing maintenance.

When you scrape website for keywords, the hardest part is to get the exact search results you need without setting up a separate manual search for every single term. You might want to pull Google search results for dozens or hundreds of keywords at once, or check how a specific site's own search function responds to a keyword list.

Search's underlying data keeps shifting too: Google tightened restrictions on API access for SEO tools in 2024, which is part of why more teams have moved toward direct scraping or multi-tool approaches to keep their keyword data current. A simpler path than manual searching is a scraping solution that automates the process and extracts results for each keyword automatically. Depending on your project's scale, that could mean a free scraper, a paid solution, or a custom tool built specifically for your needs.

Website Keyword Scraper: Use Cases & Data Type

Every use case is united by the same process: searching a keyword and extracting the results. However, fields of data differ sharply by team.

Team/Use Case	What They Extract	Example
SEO	Ranking positions, competitor content, featured snippets, related search terms	An SEO team scrapes Google results for "best project management software" across 50 target keywords to see which competitor pages rank in the top 5, then analyzes what content structure and word count those pages share.
PPC & Advertising	Ad copy, sponsored listings, cost-per-click estimates, competitor ad positioning	A performance marketing team scrapes SERP results for a product category to track which competitors are launching paid ads, what headlines they use, and how ad placement shifts by location or time of day.
Market & Competitive Analytics	Search volume trends, keyword clustering, seasonal demand shifts	An analytics team scrapes search results weekly for a set of industry keywords to track how demand for a product category rises or falls ahead of a seasonal buying period and later feed that data into a forecasting model.
Content Strategy	"People also ask" questions, related searches, content gaps	A content team scrapes SERPs for a valuable topic cluster to identify which questions Google surfaces that are not yet answered on the company's site, then prioritizes new articles around those gaps.
Local SEO	Location-specific rankings, map pack results, local business listings	A multi-location business scrapes search results for "[service] near me" across 20 cities to see how its ranking varies by market and where local competitors are outperforming it.
Brand & Reputation Monitoring	Where a brand name ranks, what surrounds it in results, negative content visibility	A brand team scrapes search results for their own company name monthly to catch negative sentiment, outdated listings, or impersonating sites.

An SEO analyst cares about ranking position and page structure; a PPC manager cares about ad copy and spend signals; an analytics team cares about volume trends over time. That's often the deciding factor in whether a free tool like ParseHub is enough, or whether a project needs a custom scraper built around the specific data fields and use case.

Website Keyword Scraper Setup: ParseHub Walkthrough

First of all, you need to install ParseHub, it's free. The next step is to set up a project in it: Click on "New Project", and indicate the URL of the site you are going to scrape data from.
With the help of settings, in the "Starting Value" section, define the keyword list to search through.
Then go back to your project with "Back to Commands", click the sign "+" that is next to your "page" selection, click on "Advanced" and then on the "Loop" command.
Click "+" next to "For each item in keywords" and select "Begin New Entry". This command will by default be given a name "list1", then click "+" next to the "list1" tool and then "Select".
With this command, click directly on the search bar of the targeted site to select it. Then make the scraper add the keyword for each result next to it (click on the "+" sign next to the command "list1" and select the "extract" command).

Google Keyword Scraper Tool: Octoparse Walkthrough

At times, you may face the need to interact with a site when extracting data. It is especially actual for cases when you need a list of keywords to be searched through, but you don't want to enter them into a search box one by one. With the built-in Octoparse browser, you can interact with the web page by pointing and clicking, just like using any ordinary browser.

The basic steps to input the text presuppose the following actions:

Select the "Click Item".
Select the input field on the webpage of the browser.
Enter the keywords into "Text list" to generate a loop search action. Every word will be automatically entered in the list into the search box, one at a time.
Then choose a keyword and click on the "Enter Text" so you can see that the chosen word is put in the search box.

Thus, Octoparse will then simulate a real browsing process and the search results of the selected word will be seen on the web page in the built-in browser. As soon as the task of how to search web pages for keywords and select data accordingly is solved, the data can be fetched from the webpage with a few clicks. All you need is only to run the task.

WebHarvy Google Keyword Scraper: Step-by-Step

First, make sure you have this keyword scraper tool downloaded and installed.
Then configure WebHarvy to extract the information displayed after each keyword submission.
You can either enter keywords one per line in the search box through the Actions menu and by clicking the Input a list of keywords or copy-paste the whole list in CSV format. You can even enter several lists of keywords according to which you need to scrape data.
Once all the necessary lists are configured, you may fill in additional parameters and get keywords from the URL(s) you target.
Click the Start-Mine button to start data extraction for all specified keywords.

How to Scrape Website for Keywords: Choosing the Right Tool

The purposes of retrieving data based on a keyword search may be variable. In some cases, a free and easy-to-deal-with Google search scraper will do, but for some more complicated tasks, you'd better turn to professional service providers. Such complications include: tracking hundreds of keywords on a recurring schedule, managing location and language variations across dozens of markets, or keeping results current despite rate limits or Google's own detection.

Document Scraping and Parsing Solutions

Sergii Klius — Mon, 19 Oct 2026 17:52:54 +0000

Key Takeaways

Document scraping finds, downloads, and tracks documents, while document parsing extracts and structures their content. Scanned PDFs require OCR before parsing.
Text, fields, tables, images, and metadata can be extracted from PDFs. The method depends on the document type: text-based, form-based, or scanned.
A complete pipeline includes collection, parsing, cleaning, validation, and data delivery. Incremental scraping also allows new and updated files to be processed automatically.

Document scraping doesn't end with downloading PDFs. A folder of reports, invoices, or regulatory documents isn't a ready-to-use dataset: tables can lose their structure, values can lose context, and scans may contain no machine-readable text at all. To make these files usable for search, analysis, or integration, collecting them isn't enough. You must also recognize the text, parse the structure, and validate the data. Together, document scraping and parsing form a single process that moves information from a web source into structured data.

How Document Scraping and Automated Document Parsing Work

The first step in deciding how to extract data from PDFs is identifying the document type. Data can be extracted directly from text-based PDFs. Scanned files require OCR and rules or templates for forms and recurring document formats.

Automated document parsing then identifies the required fields, reconstructs table structures, and maps the output to a predefined schema. This approach works well when documents follow a consistent layout, but becomes harder to maintain when the positions of fields and tables change from file to file.

In these cases, AI can help classify documents and identify fields that are difficult to define with fixed rules. Regardless of the method, the output must be validated against expected formats, ranges, relationships between fields, and the original document. A document scraping tool alone is therefore sufficient only for straightforward file downloads. Recurring processing requires a complete pipeline: collection, OCR where needed, parsing, cleaning, validation, and data delivery.

What kind of data can be extracted from PDFs?

PDF files are used for many types of documents, ranging from books, brochures, reports, and presentations to invoices, purchase orders, and bank statements. They can also contain embedded media and attachments. PDF scraping and parsing solutions are commonly used to extract:

Text content
Individual data fields, such as tracking numbers, dates, and costs
Lists and table data
Images, metadata, and embedded attachments

Despite advances in document processing, many documents, especially government records, remain unstructured and difficult to process. The method used to extract data from a PDF depends on how the document was created.

What Determines How to Extract Data from PDFs?

The processing path is determined by several criteria before data is extracted. First, the system checks whether the file contains a text layer, page images, or interactive fields. Then, it analyzes the layout, languages, and access restrictions. A single PDF can combine several characteristics, such as containing text pages, scanned attachments, and multi-page tables.

Text PDFs

This group includes reports, bank statements, invoices, catalogs, and regulatory documents exported from an office, accounting, or reporting system. They already contain machine-readable text, so OCR is usually not required.

The system reads text objects, their coordinates, and other available page properties. The parser then restores the reading order, identifies headings and fields, and matches them to the target schema. Rules or templates can be applied to documents with a stable layout. If individual pages contain only images, OCR is run only for them, not for the entire file.

Scanned and Image-Based PDFs

These are digital copies of paper contracts, signed forms, archival documents, invoices, and certificates. The text on such pages is part of the image, so it cannot be extracted directly.

Before OCR, the pages are deskewed, rotated, and denoised as needed. An OCR engine, such as Tesseract, recognizes the text and its coordinates. After that, a parser identifies the fields, reconstructs the tables, and maps the result to a predefined structure. The quality of the recognition depends on the resolution, skew, contrast, and condition of the original, so a sample of scans of varying quality is tested before full-scale processing.

Forms and Documents with Recurring Layouts

This category includes questionnaires, applications, tax forms, surveys, invoices, and purchase orders. The processing method depends on whether interactive fields are preserved in the PDF.

AcroForm contains a tree of named fields and their values, which can be read without reference to page coordinates. XFA stores the form structure and data in XML and requires a separate parsing method. If the form has been flattened, interactive fields are no longer available. Then, the labels and values are identified using text, coordinates, or a template, and OCR is first applied to the scanned form. If there are several versions of the layout, the system first classifies the document and only then selects the appropriate parser or template.

PDFs with Complex Tables

Financial reports, certification lists, catalogs, and government registries often contain merged cells, repeated headers, and tables that continue to the next page. At the same time, the PDF may not contain an explicit description of rows and columns.

For text PDFs, the table structure is restored by text coordinates, lines, and cell borders. Tools such as Tabula can be used for this task. For scanned tables, OCR is first required. Fragments of a multi-page table are linked by document and table identifiers, page numbers, and column headings. After merging, the number of columns, value types, units of measurement, and totals are checked.

PDFs with Multiple Columns

Scientific publications, annual reports, and regulatory bulletins may contain two or three columns of text. If you read the text only in the order it is stored in the file, sentences from different columns may get mixed up.

In tagged PDFs, the correct reading order is sometimes already specified by the logical structure. In other files, the system determines column boundaries by the coordinates of text blocks, processes each column separately, and then combines the result in the correct sequence.

Multilingual PDFs

International certificates, contracts, and government forms can contain multiple languages on a single page. For scans, the language is determined at the page or individual text block level and the appropriate OCR model or language set is applied.

In text PDFs, incorrect characters may be caused not by the language, but by a missing or incorrect Unicode mapping for the font. In this case, use another extraction method or OCR as a fallback. Names, dates, amounts, and identifiers are checked separately, as a single character error can change the value of a field.

Protected and Encrypted PDFs

Password protection or encryption is not a content type, but an access condition. Before processing, the system checks the protection mode and available permissions. The file can only be decrypted and passed to the pipeline if authorized access and the necessary credentials are available. Decrypted copies, intermediate files, and logs should also be stored according to security requirements.

For all categories, the original PDF should be saved along with the source, parser or model version, and processing status. Documents that do not match the expected profile, such as a text PDF with a scanned page or a form without a required field, are sent for reprocessing or manual verification. Separate scraping and parsing steps allow you to change the rules and reparse the saved file without downloading it again from the source.

What Does DataOx Do, Exactly?

First, we collect the required documents and parse their content. We then clean the extracted data and format it as XLSX, CSV, JSON, or XML using a structure compatible with the client's storage system and requirements. Documents can also be tagged for search or categorization, stored in the client's software ecosystem, or delivered via SFTP or directly to a database.

Incremental document scraping

Clients often need to monitor web sources for new or updated documents. In this case, we configure a data feed that collects, structures, and delivers these files according to the agreed schedule.

Example of a Document Scraping Project

In the legal research project we worked on, DataOx collected, transformed, and formatted statutes and other legal documents from all 50 U.S. states.

Each U.S. state presented statutes and legislative documents differently, so we developed rules that mapped them to a single structure. The source material lacked a consistent machine-readable structure, so the data was parsed and mapped to a unified format. The pipeline also integrated multiple legal information sources and was maintained as those sources changed.

As a result, the client received a curated dataset ready for use in an AI/ML pipeline and built a powerful intelligent engine based on it.

How to Scrape Quora Data for Content and Market Research

Sergii Klius — Mon, 19 Oct 2026 17:47:06 +0000

Key Takeaways

A Quora question scraper only collects a visible sample of user content, so the number of answers, votes, and author activity can't be directly interpreted as demand, sentiment, or expertise.
Reliable data collection from Quora requires a clear data collection structure, pilot testing, full dynamic pagination, ID-based record linking, validation, and regular updates.
To use Quora answers in research, preserve their context, analyze scenarios within segments, and cross-check the patterns you find with other sources.

A Quora scraper turns questions, answers, and profile data into a structured dataset that can be regularly updated and analyzed for content and market research.

In this article, we'll explain how to organize Quora scraping properly: define the data you need, collect it, validate the output, and keep it up to date.

What to Know Before Scraping Quora for Research

A Quora question scraper doesn't directly measure market sentiment or trends. It collects user-generated content that Quora's algorithms display under specific conditions. Therefore, even full pagination doesn't guarantee reliable results. You also need to understand how the sample was formed: where the questions were found, who could see and answer them, and what content was included in the dataset.

Search results don't cover the entire topic

Data gaps can appear as early as the question search stage. Topics on Quora are created by users, so the same problem may be described differently and discussed under several Topics. Therefore, searching by a single keyword or Topic captures only part of the discussion, and the list of Topics doesn't show the full market picture.

To build a broader sample, create a query matrix with synonyms, different descriptions of the problem, brand names, and related Topics. For each question found, store its qid, the search query, and the discovery source.

An analysis of millions of topic pairs showed that Quora users took an average of 936 days to identify Topics that should be merged. Duplicates and different names for the same concept can coexist for years.

Answer count doesn't measure demand

The number of answers doesn't always show how interested the audience is in a topic. Even a user with relevant experience must first see the question and then decide to answer it. Therefore, a question may receive no answers simply because it didn't reach the right audience.

The wording of a question is closely related to whether it receives an answer. By examining the linguistic features of a question, it was possible to predict with 76.26% accuracy whether it would receive an answer within a month. Therefore, a large number of answers may be associated with effective wording and doesn't, by itself, prove market demand.

The number of upvotes depends on prior visibility

An answer's popularity on Quora depends on more than its content. Users often judge authors by their previous contributions. As a result, well-known authors and answers that already have many upvotes attract more attention.

Position in the list also matters: the higher an answer appears, the more users see and upvote it. Therefore, the most popular answer may not be the most accurate and may not reflect the views of most users.

Profile credentials don't confirm expertise

A large number of answers and credentials listed in a profile don't necessarily mean that the author is an expert. To assess their experience, look at the topics they answer questions about, how other users respond to their posts, and how they interact with the community. High activity may only indicate an interest in the topic. Therefore, a Quora profile scraper should collect profile data, answer history, and the credentials the author displays next to each answer. This data helps you better understand the author's experience but doesn't confirm their expertise.

Not every answer is an independent human opinion

Quora includes human-written, AI-generated, translated, and platform-promoted answers. Without separate labels, they will be mixed together in the same dataset, which can distort the analysis. Therefore, for each answer, store the publication date and content type when Quora provides this information.

How to Use a Quora Scraper Step by Step

Quora data collection is better built as a pipeline rather than a single script.

Step 1. Define the Scope and Dataset Structure

To collect data that will really help your research, first decide what you want to find out and what records you need to do it: questions, answers, or profiles. Then decide which fields to store and how the records will be related to each other.

Record the qid for questions, aid for answers, and uid for profiles, as well as the source URL, publication date if available, and time of collection. For each answer, additionally store the text, author, their qualifications, number of votes, position in the list, and content type.

Step 2. Build a Sample of Questions

A single search query is not enough to capture the many different ways users describe the same problem. So, you need to create a matrix of keywords, synonyms, brand names, and alternative wordings.

Search for questions through Quora search, Topics, Spaces, profiles, external search engines, and direct URLs. For each question found, record the qid, query, source, position, and discovery time. If the same qid is found through multiple sources, keep one record, but preserve all the ways it was found.

Step 3. Choose a Collection Method and Run a Pilot

The method of collection depends on what data you need. If you already have the URL for a specific question, answer, topic, or profile, you can get the basic metadata directly from the HTML using an HTTP client. Quora loads search results, lists of answers on profiles, and subsequent answers via XHR, so a browser-based scraper is required to collect them in full.

This division is confirmed by a recent technical breakdown. Before a full run, test the method on questions with different answer counts, profiles, and Topic pages.

Step 4. Collect the Related Records

Don't assume collection is complete after a fixed number of scrolls. Store the question and the answer count shown by Quora, then load subsequent data batches while each batch adds new aid values and returns a new cursor.

Stop collecting if the cursor is missing or repeated, or if no new aid values appear. A Quora profile scraper should store author profiles separately and link them to the answers using uid. When working with Playwright, it's better to wait for a new record or the required page state than to set the same delay after every action.

Step 5. Verify the Dataset and Set Up Updates

Regular runs should only be set up after you have validated your first set of data. Remove duplicates by qid, aid, and uid, check the relationships between questions, answers, and authors, and manually cross-check several records against Quora. A value of 0 means that the metric is truly zero, while null means that the value is missing or unknown.

Store blocked, restricted, and partial as separate statuses. For each run, record any URLs that couldn't be processed and the run results. On subsequent runs, look for new answers and recheck previously collected records for edits, deletions, and changes to metrics, positions, redirects, and access status.

Best Practices for Scraping Quora for Research

Collected answers can only be used in research if the context in which they were published is preserved. Let's imagine that a company wants to find out why small teams are switching to other CRM systems.

Define Analysis Rules Before Full Collection

Don't limit your analysis to whether the user is satisfied with the CRM system or not. For each answer, capture the user's context, the reason for switching, the problem, the alternatives considered, and the outcome. These details show what lies behind the user's overall assessment.

For example, the statement "The CRM has become too expensive" doesn't explain much on its own. But with context, it points to a specific reason for switching: it was written by a member of an eight-person team, the price increased as the number of contacts grew, and costs decreased after the team switched to a different CRM.

Test this analysis framework on 50–100 answers. If a team labels the data, have two analysts independently label the same 20–30 records. Their disagreements will show which categories need to be refined.

Don't Reduce Answer Quality to One Score

The popularity of an answer doesn't always indicate how useful it is for your research. A short answer with three upvotes, specific steps for switching to another CRM system, and a mention of the CRM feature that caused the problem may be more valuable for product analysis than a long, popular answer with general observations.

So don't reduce answer quality to a single score. Evaluate each criterion separately: the answer's relevance, the author's firsthand experience with CRM systems, the answer's specificity and recency, and its visibility on Quora. The weight of each criterion depends on the task.

For content research, users' wording and recurring questions may be more important, while for competitor analysis, users' firsthand experience with CRM systems and their reasons for switching from one system to another may matter more.

Analyze Scenarios, Not Mention Counts

The number of mentions doesn't always indicate the importance of the issue. If 12 responses are about price and only three are about data export difficulties, that doesn't mean that export is less important. For banks, insurance companies, and healthcare organizations, limited data export can be a major reason for CRM abandonment.

To preserve this context, keep the question, answer, author and team information, date, and source of discovery as a single record for analysis. After labeling, combine the records into a matrix with the following fields: segment, reason, problem, alternative considered, and outcome.

Don't discard rare or contradictory responses. Formulate the conclusion within the segment: "Export issues are common among teams in regulated industries," rather than "The market is unhappy with data export." This will make it clear which segment the observation applies to.

Check Quora Insights Against Other Sources

Use Quora to form hypotheses, not definitive conclusions. If users frequently mention a difficult CRM setup, check to see if the issue recurs in reviews, search queries, support requests, and professional communities. A pattern that recurs across multiple sources reinforces the hypothesis. Mark a signal found only on Quora as platform-specific.

This approach is already used in other DataOx projects. In the organic platform case, more than 500,000 records updated each month were combined into one system from dozens of sources. In the HR platform case, data comes from Indeed, LinkedIn, and more than ten other platforms.

Using the same model, a Quora pipeline can combine questions, answers, and profiles with reviews and other research sources. A unified structure, common analysis rules, source provenance, and regular snapshots make it possible to trace every insight back to a specific answer, context, source, and date.

How to Scrape Telegram Channels and Groups

Sergii Klius — Mon, 19 Oct 2026 17:22:42 +0000

Key Takeaways

Telegram provides a free official API. So, the main challenges lie in following privacy regulations, adapting to MTProto, and managing request frequency.

Pulling data via the API requires writing a Python script using a specialized Telethon library.

For use cases that require other, more protected social media sources, getting a pre-made tool or custom integration is often more efficient than scraping in-house.

Scraping sources that offer a free official API can seem easy. However, the process has its own pitfalls, especially when you try to scale up. In this article, we guide you through setting up a scraper for Telegram and warn you about the common challenges you may encounter when collecting and using the information.

The Main Challenges of Running a Telegram Scraper

The platform offers two official free APIs for bot development and client access. This makes it different from most social media sites that strictly limit automated access. Even though you don't have to bypass aggressive protections, there are still several challenges you can face.

1. Data regulations

Messengers host a lot of information that can be considered private or sensitive in many jurisdictions. That includes phone numbers, usernames, and messages in large groups with invite-only access.

Do your legal research before creating a Telegram scraper bot and limit your scraping where possible. When storing the scraped information, make sure you skip the private details and anonymize the records.

2. Learning curve

If you're used to pulling standard HTML, building a Telegram channel scraper can be unusual. The process requires working with binary MTProto and managing the API connection. HTML requests are still an option for preview pages, but the output is limited. Previews display channel metadata and around 20 recent posts for each source. For the precise number of members, comment texts, and full post history, an API is a must.

3. Data access

Getting full access and using the official API requires authentication. You need a valid account that can join channels and groups to collect information visible to members. If you exceed the limits and trigger protections, the account can get banned. Note that fresh accounts can look more suspicious than the ones that are at least a few months old.

4. Rate limits

Though the messenger is quite flexible in terms of public information collection and has no stated rate limits, the anti-spam system is sometimes an unexpected challenge. The system can flag your Telegram members scraper as a first step for sending spam to the collected accounts. Here are the common errors and what they mean.

Error	Triggered by	Meaning
`FloodWaitError`	Frequent `GetParticipants` calls	You need to add wait periods
`PeerFloodError`	Ignoring `FloodWaitError`; combining scraping members with adding users to chats or channels in the same session	Your account is classified as a spam risk

You can rotate proxies and use multiple accounts to bypass these limitations. There is also a way to gather active user data without directly pulling member lists. Extract comments and group messages and pull usernames from them.

5. Discoverability

Messengers are not easily searchable, especially when it comes to private channels and groups. For comprehensive analytics, you'll need to find the specific sources and invite links before launching a large-scale Telegram channel scraper.

A Step-by-Step Telegram Scraping Guide

The fastest and most efficient way to get the information is to use the official API. Here is a brief guide to setting it up for in-house teams familiar with Python.

Install Python if you don't have it yet.
Install Telethon, a specialized Python library created specifically for this use case.
Get access. You'll need an account and an individual ID and Hash for the API. Go to my.telegram.org to sign up and open API development tools to register your future application.
Set up Telethon by entering your API ID and Hash.
Set up proxies. This is an optional step, as the platform doesn't block IPs as harshly as most social media sites, but you can choose to do it to protect your address or build a large-scale pipeline. Telethon has no built-in proxies, so you'll have to get a third-party provider.
Choose the sources you want to scrape. You need to specify channel names or links to start scraping. For a Telegram group scraper to access private group chats, you have to be a chat member or provide a valid invite link.
Scrape the content you need. If you're building a Telegram members scraper, limit requests per session or extract user details from message data.
Store the output. For accurate analysis, make sure you get structured information with stamped metadata like post and collection time, source channel, and author.

If you don't have the technical capabilities to build your own setup, there are plenty of no-code solutions. In some instances, they can be more efficient than in-house development. The choice depends on your use case.

Use Cases for Scraping Telegram Channels and Groups

The collected information is used for scientific and business research across industries. In some instances, Telegram can replace more protected sources for intelligence monitoring. Here are a few examples how businesses use Telegram channels and group data.

1. News tracking

Large media sites like the BBC or Reuters limit access to their publications and use aggressive anti-bot measures. Scraping news in messengers via an API can be a faster and cheaper alternative to rotating residential proxies and using other advanced tools to overcome protections.

In many countries, big reputable media have official Telegram channels with summaries of the most important news. Even if it's not the case for your market, mid-tier and reposting channels can be useful for event tracking.

2. Brand monitoring

A brand monitoring tool can't be effective if it covers only news sites and press release distributors. Customers discuss businesses across forums, social media, and messenger groups. Scraping conversations and comment sections helps identify a potential reputation crisis before it affects a business's sales.

3. Sentiment analysis

Group chats and personal blogs are valuable sources for large-scale sentiment signal tracking. They can help assess public opinion about a business, technology, or political party.

Scraping specific niche channels and groups provides a detailed overview across different audience segments. Telegram stores content indefinitely, allowing researchers to see how public opinion evolved and spot channels and influences that have played their part in each change.

However, one messenger is hardly sufficient for comprehensive sentiment research. According to Statista, Telegram ranks seventh worldwide. Scraping more popular sites like Facebook will require more resources and expertise.

4. Market signal tracking

Crypto markets are sensitive to the smallest shifts in the political environment and public sentiment. A scraper can send event notifications and extract conversations where early concerns appear.

5. AI training

Real-world data is increasingly rare and valuable for training new AI models. Messengers allow you to gather fresh discussions and publications at scale. Content from small blogs often includes niche expertise necessary for industry-specific models.

Pre-Built Scraper for Telegram vs. Custom Scraping Services

In-house scraping works for simple projects and teams ready to invest in hiring and retaining engineers for development and maintenance. Here are a few signs that professional scraping might be a better option for you:

you don't have an in-house development team;
your vibe-coded solution repeatedly breaks;
you need a reliable infrastructure for ongoing monitoring;
you want complex data integration, not a raw file.

If you meet one or more of these criteria, the next step is to choose a tool or a team to handle the scraping. Here is a quick decision matrix:

Do you need data today and only today? If yes, opt for an Apify Telegram scraper or a similar off-the-shelf tool. Custom services take days to weeks depending on project complexity.
Do you need continuous monitoring? Either option can work. However, if you choose a ready-made tool, be ready to cover part of the maintenance on your end of the data pipeline.
Do you need ready-to-use data integration? If so, choose a service company that can handle the process end-to-end.

Ready-made crawlers are designed for the most common use cases and often have limited adaptability. If you need the data integrated into your product or workflow, custom services are a more reliable option.

How to Scrape Resale Marketplace Data: StockX, Depop, Vinted

Sergii Klius — Fri, 09 Oct 2026 16:20:30 +0000

Resale marketplaces lack official APIs and limit automated data access. Collecting their data requires a Depop scraper that can imitate human behavior. For resale market analytics, multiple source-specific scrapers need to be integrated into a single system. The article explains the best practices for such integration and compares pre-built and custom options.

The global resale market grows twice as fast as apparel retail. In the US, the contrast is even more stark: 4X, according to ThredUp's Annual Resale Report. Future growth will be driven by AI that is able to connect supply and demand. In this article, we explain how to gather the data needed to develop AI matching tools, analytics platforms, and resale market intelligence instruments. Find out how to go from a Depop scraper to a multi-source integration for real-time insights.

The Main Challenges of Scraping Resale Platforms

Like most e-commerce platforms, resale marketplaces use various anti-bot measures to spot scrapers and limit automated access to their data. Here are the most common problems businesses face when building a pipeline for secondhand market intelligence.

1. Limited data access

Most fashion resale platforms have no official APIs that could give access to search results, shop listings, or market data at scale. The existing endpoints are made for sellers with premium accounts (i.e. Vinted API) and help automate shop management. Here is an overview of data access options and barriers for popular websites.

Platform	Official API	Anti-scraping protection
Depop	Private Selling API (not for data collection)	Cloudflare
StockX	REST API for selling activities	Cloudflare, PerimeterX / HUMAN
Poshmark	No official API	Cequence Security, TransUnion TruValidate Device Risk
Vinted	API for allowlisted Vinted Pro businesses only	Cloudflare, DataDome

The table shows that building a StockX or Poshmark scraper is the only option in many scenarios. However, a scraper will meet its own challenges due to aggressive anti-bot protections. 403 Forbidden error is the most common response beginners get. When deciding whether to grant you access, the server protection system evaluates:

IP reputation. You can't run a Vinted scraper on blacklisted datacenter proxies. High-quality residential or mobile IPs are a must.
Fingerprinting. The signals of your device and browser need to be consistent and look realistic.
Session behavior. This includes mouse movements, rate frequency, random pauses, and more.

To avoid triggering the protection, a StockX scraper needs to imitate the technical signals and real-time behaviour of a human. Moreover, bypassing the blocks once does not guarantee ongoing success.

2. Frequent updates

The lack of official APIs leaves your Depop or Poshmark scraper with two options:

reverse-engineering internal APIs;
extracting data from loaded pages.

Both are unstable due to website updates. Every time the platform layout changes, an HTML-based scraper starts returning empty fields and needs to be adjusted. Internal APIs stop working unexpectedly, forcing your team to search for updated endpoints and restart the reverse-engineering process. You need to handle these changes on top of the evolving anti-scraping measures.

3. Regional inconsistencies

Secondhand markets vary significantly across countries. However, even if you don't need region-specific structured price data, make sure your Vinted scraper has consistent geo markers throughout the pipeline. Random language and location signals look suspicious to the platform's protections.

4. Scattered data

No single website covers the whole resale market. An Apify Vinted scraper Actor provides limited insights when 50% of buyers find secondhand items through social media and influencers, according to ThredUp. For comprehensive analytics, you'll need to integrate scrapers from multiple sources.

Best Practices of Multi-Source Scraping

The key to building a reliable intelligence pipeline is adding enough checks and safeguards so that one source change does not cause the whole system to crumble. Below are a few steps to follow when building your scrapers.

Separate your scraping architecture. Building distinct scrapers and parsers keeps your system flexible and makes maintenance easier. Your StockX scraper will likely require different extraction and bypass methods than the one for Vinted. Choose tools that work best for each source and unite the data downstream.
Adapt extraction layers. Multi-source projects often mix structured (JSON/XML/CSV) and unstructured (HTML/images) output, especially when using off-the-shelf tools like an Apify Vinted scraper. Add source-specific parsing to unify data formats.
Deduplicate and match entities. Many sellers list their products on multiple platforms at once, which can affect your statistics.
Normalize the data. Make sure you transform currency, sizing, and account for localized brand names if they matter to your analytics.
Store raw data. Integration often requires running multiple transformation steps. Keep the freshly scraped data before changing it to check for errors later.
Add validation and health checks. Even if you use a ready-made Depop scraper API that claims to handle maintenance, it can return empty or incomplete fields without warning.

These best practices help you build a scalable system ready for new data connections.

Pre-Built Vinted and Depop Scraper vs. Custom Services

Most off-the-shelf marketplace scrapers allow for a certain degree of integration and can be built into an analytics workflow. The difference between such tools and custom services lies in flexibility and the amount of work a client has to handle in-house. Here is a brief comparison.

	Pre-built scraper	Custom services
Example	Apify Vinted scraper	DataOx services
Maintenance	Scraper health handled by a third-party developer from the Apify community; data completeness and integration pipeline maintenance handled by you	DataOx maintenance package covers scraper breaks, data issues, integration and visualization problems
Integration	Apify API with endpoints for Make, n8n, Zapier, and other popular services	Handled end-to-end by DataOx, including target dashboard or web app development
Data unification	Handled by you	Handled by DataOx

Note that Apify is considered one of the best options for off-the-shelf crawler integration. A Depop scraper API from another provider can require even more effort on your part. If you need to connect multiple scrapers, maintenance can become chaotic, with a handful of different providers working on their own schedule. Choosing one team for custom development can solve this problem.

Pinterest Scraper: How to Collect Pins, Boards & Profiles

Sergii Klius — Fri, 09 Oct 2026 16:07:45 +0000

Key Takeaways

The goal of a Pinterest scraper isn't just to collect as many Pins, boards, and profiles as possible. It helps track search visibility, competitor activity, the spread of creatives, and changes over time.
Reliable Pinterest data depends on complete pagination, deduplication, consistent collection conditions, and result validation. Only then can real changes in the data be separated from collection errors.
Pinterest Trends data, new content, and search visibility should be analyzed together. This helps explain why a brand is gaining or losing visibility and what the team should do next.

With a Pinterest scraper, you can collect hundreds of records, but it may still be unclear whether all the collected data is relevant and whether anything is missing.

In this article, we explain how to verify the source, remove duplicates, confirm data completeness, and determine when a ready-made tool is sufficient and when a custom data collection process is needed.

What Exactly Does a Pinterest Scraper Observe?

If Pinterest Trends shows the overall change in interest around a topic, a Pinterest scraper helps reveal what lies behind that growth. The scraper collects specific Pins, profiles, boards, positions, and external links to show which brands attract attention, which creatives they use, where they send users.

A single collection only shows the state of the data at a given moment. When it's repeated regularly, the changes become visible:

Search visibility. A Pinterest pins scraper records the query, position, region, time, and Pin ID. Comparing the results shows which topics helped a brand gain visibility and where it fell behind competitors. The team can then adjust its keywords, creatives, or campaign timing.
Competitor activity. A Pinterest profile scraper and Pinterest board scraper track new Pins, boards, topics, and external URLs. This helps teams spot a new collection, seasonal campaign, messaging change, or landing page in time.
Visual spread. A Pinterest image scraper finds identical and similar images across different Pins. This shows which styles the market is picking up, how competitors adapt them, and which sellers' websites these creatives lead to.
Growth rate. The total number of saves reflects past performance. Changes between several collections help identify Pins that have started growing now. This may be an early sign of a new topic or creative direction.

However, saves don't show a Pin's effectiveness without impression data. These metrics are available through Pin Analytics only for Pins owned by the account or on shared boards the account can access.

Together, these data points help teams decide which topic to use in a campaign, which creative to test, and which competitor actions require a response.

Whether the team builds its own tool after searching for Python Pinterest scraper or chooses a ready-made Apify Pinterest scraper, the first step is to define which changes it needs to detect. Otherwise, even 100,000 collected Pins will remain just 100,000 rows.

Pinterest Data Collection Workflow: Six Practical Steps

Pinterest loads Pins gradually, so an open page shows only part of the data. The entire process can be divided into three stages.

1. Data Source and Collection Method

For boards owned by or accessible to the account, the Pinterest API remains the most stable option. Of course, sometimes a ready-made solution (like Apify Pinterest Scraper, Browse AI, or Octoparse) may be sufficient. For example, in case if you need a one-time collection from a public board, profile, or search.

In a custom Python Pinterest scraper, Selenium or Playwright loads the page, while the scraper retrieves data from network responses. If the required information is available only in the HTML, it can be processed with BeautifulSoup or the DOM.

2. Test and Full Collection

After the collection method is selected, a small test is run on one board, profile, or search query. It shows whether the required data is available and whether the next Pins load correctly.

If the test succeeds, the full collection begins. Pinterest loads Pins in batches, and the scraper moves from one batch to the next until no new data remains. This is why the number of page scrolls doesn't show whether all available data has been collected.

3. Preparing the Result

When the collection ends, all batches are combined. Exact duplicates are removed, repins remain linked to the original Pins, and identical images are marked separately.

The data can then be delivered as CSV or JSON, or sent to a database or API. For recurring monitoring, the system also saves the time and stopping point of the previous run so that the next run retrieves only new and changed records.

How to Validate Pinterest Changes

You may have encountered a situation where a Pin appears in the first collection run but disappears in the next. At first glance, it seems that something has changed on Pinterest. However, the cause may simply be a collection error. To avoid false alerts, you need to separate a technical error from a real change. This requires three checks in sequence:

First, check the collection run itself. Both runs must cover the same board, profile, or search query and finish without errors. For API collection, pagination ends when Pinterest no longer returns a bookmark. If the process stops earlier, a missing Pin should be treated as a missing record, not as a change on the platform.

Then compare the conditions. You can only compare results collected with the same query, region, authorization status, and collection depth. You should also record the time of each run. This matters because Pinterest search considers both the query and user data, as confirmed by Pinterest's technical research.

Only then draw a conclusion.

If a Pin disappears from search results, it only means that it no longer appears in the collected portion of the results. This doesn't prove that it was deleted. An increase in saves shows new momentum, but without impression data, it doesn't confirm higher effectiveness. The same image with a new Pin ID may also indicate that the visual is spreading rather than being duplicated.

After validation, the event receives one of three statuses:

confirmed change;
observation requiring another check;
collection error.

Only changes confirmed by complete and comparable runs are included in a report or alert.

Why Does a Brand's Visibility Change on Pinterest?

When a competitor begins appearing more often in search results, the reason isn't always obvious. The topic itself may be gaining interest, the competitor may be publishing more Pins, or Pinterest may have changed how it distributes visibility.

To distinguish between these scenarios, compare three signals:

Interest in the topic, according to Pinterest Trends.
Content supply: how many new Pins, visual concepts, and external domains have appeared.
Position-weighted visibility: how often and how high this content appears in search results.

Together, these signals help explain what's happening. If interest is growing but few new creatives are appearing, the market may not yet be meeting demand. If interest remains unchanged but a competitor publishes more Pins and gains higher positions, this may indicate an active content campaign. If publishing volume grows but visibility doesn't, volume alone isn't helping: the problem may lie in the visual, relevance, or product offering.

This is where a Pinterest scraper is needed. Pinterest Trends shows overall interest, while Pin Analytics shows the performance of Pins owned by or accessible to the account. Scraping adds the missing view: what competitors publish, which domains appear in search results, and who Pinterest is actually giving visibility to.

This analysis will become more important because Pinterest search already combines queries with user history and optimizes results for different actions, including product clicks, as explained in the PinRec study. Therefore, knowing a single position won't be enough for businesses. They need to understand what exactly changed the balance of visibility and what action it requires.

Web Scraping Challenges: 8 Obstacles and How to Fix Them

Sergii Klius — Thu, 08 Oct 2026 15:54:10 +0000

Key Takeaways

Core web scraping challenges divide into three categories: structural (site redesigns, inconsistent HTML across pages), technical (JavaScript rendering, dynamic content loading), and defensive (bot detection, CAPTCHA, IP blocking, browser fingerprinting, honeypot traps).
Anti-bot detection is layered. Enterprise protection from Cloudflare, Akamai, Imperva, DataDome, and PerimeterX combines IP reputation, fingerprinting, and behavioral analysis.
Proxy type has to match the target's protection level. Datacenter IPs work for lightly protected sites, while residential or mobile IPs are usually required for enterprise-grade bot management.
Data quality and duplicate data challenges are prevented with a validation step before delivery.

As web scraping keeps getting more popular, more obstacles stand between a scraper and clean, usable data. Bots now make up over half of all internet traffic, and bad bots specifically account for around 37-40% of it, which explains why nearly every significant website invests in some form of bot detection today, and presents new web scraping challenges.

Below are challenges in web scraping that come up most often, and what actually works to solve each one.

Website Structure Changes and Inconsistent HTML

From time to time, websites go through structural changes or redesigns to improve user experience. This is a real challenge for scrapers built around specific page elements, even a minor change can silently break extraction. Such issues are resolved by constant monitoring and timely adjustments and set-ups.

On large sites with 1,000+ pages (big e-commerce platforms are the classic example) different pages often carry different HTML structures entirely. This usually happens because large sites accumulate old and new pages built by different development teams over years.

Solution: Scan the whole site to map where the coding differs, then adjust parsers accordingly for each pattern found.

JavaScript Rendering and Dynamic Content

Many of today's websites load their real content after the initial page request, through JavaScript. Single-page applications, infinite scroll feeds, and dynamically rendered pricing are all common challenges in web scraping now. A scraper that only reads the raw HTML response will miss such data.

The standard fix is a headless browser (Playwright, Selenium, or Puppeteer) that executes JavaScript the way a real browser would before the page content gets parsed. This adds overhead compared to a simple HTTP request, but it's required for any site with meaningfully dynamic content.

Common Web Scraping Challenges and Solutions: Anti-Scraping Techniques and Bot Detection

Detection today typically layers several methods together: bot detection scoring, CAPTCHA challenges, IP blocking, browser fingerprinting, and honeypot traps. Detection in modern systems is rarely a single-layer operation, each protection layer compensates for gaps in the others, which is precisely what makes bot detection difficult to defeat with a single simple countermeasure.

Here's a closer look at the most common dilemmas.

Web Scraping Challenges and Solutions: Enterprise Bot Protection (Cloudflare, Akamai, Imperva, DataDome, PerimeterX)

Imperva and Akamai were early leaders in bot detection and content protection. Today, Cloudflare Bot Management, DataDome, and PerimeterX are just as common, particularly on sites that get scraped often enough to justify the investment. In independent testing of nearly 17,000 popular domains, advanced anti-fingerprinting bots were blocked by only about 7% of targets, so most organizations remain highly vulnerable to proper scraping despite having some protection in place.

These systems distinguish real visitors from crawlers using bot scoring, request pattern analysis, and increasingly, AI-based behavioral modeling. Well-built scrapers today can simulate human behavior convincingly enough to get past a meaningful share of these defenses. Still, doing so consistently across many sites is a specialized skill rather than a default capability.

Browser Fingerprinting

Beyond IP and request-pattern checks, modern anti-bot systems inspect the browser itself. Canvas rendering, WebGL output, the navigator.webdriver flag, and TLS handshake signatures (JA3 fingerprinting) all leave a detectable trace that a standard headless browser doesn't hide by default. A scraper can pass all the previous checks but still get banned because of a fingerprint mismatch.

Addressing this requires fingerprint patching (tools like playwright-stealth or anti-detect browser infrastructure for high-volume projects) configured specifically to the target site's detection layer.

CAPTCHA Resolving Challenge

CAPTCHA requests are designed to separate humans from crawling tools, using logical puzzles or character-recognition challenges.

Solving them has gotten easier through open-source tools and CAPTCHA-solving services, but all cases are different. CAPTCHAs on some Chinese-language sites, for instance, are difficult even for humans; DataOx has specialists who solve these manually to guarantee smooth passing.

IP Blocking Challenge

IP blocking is another common method to fight against scrapers. It works when a website detects lots of crawling attempts from the same IP address or when the requests are coming from IP addresses already registered in the blacklists. There is also IP blocking through geolocation when a site is protected from attempts from certain locations.

The standard fix is proxy rotation, but the type of proxy also matters.

Datacenter IPs are the cheapest and fastest to acquire, but they're also the most heavily flagged because legitimate human traffic rarely originates from cloud hosting ranges.
Residential proxies, tied to real ISP subscribers, carry a cleaner reputation.
Mobile residential proxies go further still, since they're identical to smartphone traffic on a carrier network.

Which tier makes sense depends on how aggressively the target site is protected: datacenter proxies are fine for lightly protected sites; enterprise-grade bot management requires residential or mobile IPs.

Honeypot Traps

Some sites embed invisible links or form fields on a page. It is invisible to a human visitor, but present in the raw HTML and therefore visible to a scraper. Following one of these "honeypot" links or filling a hidden field is an instant signal that the visitor is a bot, and it triggers an immediate block.

Such traps should be recognized by hidden elements via CSS (display:none, zero opacity, off-screen positioning) and carefully avoided.

Common Web Scraping Challenges and Solutions: Large-Scale and Real-Time Scraping

When parsers are constantly monitoring web pages for changes, any instability in the pipeline can cause breakdowns. One failed request in a long chain can stall an entire real-time data collecting.

A system that constantly handles real-time scraping needs:

retry logic;
monitoring for silent failures;
a proper infrastructure that can handle bursts of concurrent requests.

Web Scraping Challenges and Solutions: Data Quality and Duplicate Data

The same listing, article, or product can appear multiple times across different URLs or pagination states, and without deduplication logic, that repeated data ends up in the final dataset. Running a proper quality assurance pipeline before data delivery is the only reliable way to prevent this.

Such a pipeline should include:

validating every field;
checking for duplicate records;
flagging anomalies.

Some of this can be automated; some needs manual review, particularly for complicated cases.

How DataOx Handles Challenges in Web Scraping

Every challenge above is one DataOx deals with routinely. Proxy rotation across datacenter, residential, and mobile tiers; fingerprint patching; CAPTCHA solving, including manual checks for the hardest cases — all of this is built into how we set up our projects. Structural change detection and scheduled maintenance keep pipelines running as target sites evolve, and every delivery goes through a data quality pass before it reaches a client.