In this post, I will show you how I’d use the GLM-5.2 API for a long-running coding agent.

If I were setting up a coding agent today — the kind that’s supposed to run for hours, work through a real codebase, and come back with something actually finished rather than a half-baked first attempt — GLM-5.2 api is where I’d start.

Not because it’s the flashiest name in the room, but because long-running agents fail in a very specific way, and this is one of the few models actually built with that failure mode in mind.

The Problem Nobody Warns You About Until It Happens

Here’s what nobody tells you before you build your first long-running agent: the failure isn’t usually a dramatic crash. It’s slower and quieter than that. The agent starts strong, makes good decisions for the first stretch of a task, and then somewhere around hour two or three, it starts losing the plot.

It forgets a constraint it agreed to earlier. It re-does work it already finished. It confidently “fixes” something that wasn’t broken. None of this shows up in a quick demo, because demos are short. It only shows up once you actually let the thing run.

That’s the exact scenario GLM-5.2 was built around, which is why it’s the model I’d reach for first rather than whatever’s topping a general leaderboard that week.

Why I’d Trust It to Actually Stay on Track

What sold me isn’t a single benchmark number, it’s the pattern across all of them. GLM-5.2 keeps showing up strongest specifically on the evaluations that simulate messy, hours-long engineering work rather than quick, self-contained problems.

On the kind of test that mimics an agent looping through a real repository — reading, planning, executing, checking its own work — it holds up close to the very best models out there, closed or open. That’s a different skill than being clever on a single tricky question, and it’s the one that actually matters once your agent’s task list is measured in hours instead of minutes.

Giving It Room to Actually See the Whole Project

The other piece I’d lean on hard is how much it can hold in view at once. A long-running agent working across a real codebase needs to see more than the one file it’s currently editing — it needs the surrounding context, the conventions the rest of the project follows, the pieces it touched three steps ago. GLM-5.2’s context window is big enough to hold a genuinely sizeable project in view all at once, instead of forcing the agent to work blind, one small slice at a time, and hope nothing important got left out of frame.

That’s less “nice to have” and more the actual difference between an agent that understands what it’s working inside of and one that’s just guessing.

If you’re building this kind of setup yourself, it’s worth spending a little time in the GLM-5.2 api listing before committing to anything — seeing what it sits next to, and what the nearby options trade off, tends to clarify what you actually need a lot faster than reading a single spec sheet in isolation.

The One Feature I Didn’t Expect to Care About This Much

GLM-5.2 can also be paired with a web-search tool during a task. Instead of switching to a separate model version, the agent can invoke search when it needs current documentation, recent release notes, or an external source to verify an assumption. That sounds like a small add-on until you picture what a long agent run actually needs.

A coding agent working for hours doesn’t just need to reason about the code in front of it — it occasionally needs to check something outside its own head. Did a library’s API change since the model’s training data was collected? Is there a known issue with the exact error it just hit? Rather than the agent confidently guessing at an answer that might be out of date, it can actually go look, mid-task, without a separate setup. For anything that touches fast-moving dependencies or recent library changes, that’s the difference between an agent that occasionally hallucinates its way past a stale assumption and one that just checks. 

Where I’d Actually Draw the Line

I wouldn’t pretend this is the right tool for everything, and I’d be doing you a disservice if I acted like it was. If the job is a tight, well-specified algorithmic problem — something closer to a competitive programming puzzle than an open-ended engineering task — there are other models built specifically to be faster and cheaper at exactly that kind of question, and I’d route to one of those instead rather than defaulting to the same model for every job.

The honest way to think about it is that GLM-5.2 is the specialist for staying coherent across a long, messy, multi-step project, not the fastest or cheapest option for every single request an agent might make along the way. A GLM-5.2 API for coding comparison against a more math-and-algorithms-focused alternative is worth reading before you commit your whole pipeline to one model, because the right answer genuinely depends on which of those two failure modes worries you more.

What I’d Actually Build

If I were setting this up for real, I wouldn’t hand every single request to the same configuration. I’d let the long-horizon planning and the bulk of the repository work run through GLM-5.2, with the web-search variant switched on specifically for the moments the agent needs to check something outside its own training — a library version, a recent breaking change, a fix someone already published.

And I’d keep a faster, narrower model on standby for the quick, tightly scoped questions that don’t need any of that context or reasoning depth. That’s not a compromise so much as it’s just matching the tool to the job, one more time, the same way I’d want any agent I trust with real work to be built.

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In this post, I will be talking about protecting business data when teams work from different locations.

The transition to remote work has shifted the approach companies use for safeguarding sensitive data. When staff members access company systems from geographically dispersed locations, traditional perimeter defences lose effectiveness, creating new entry points that adversaries seek to exploit. Threat actors frequently target remote desktop connections as these often present vulnerabilities for business networks.

Since 2020, there has been an increase in cyberattacks aimed at remote access systems. Attackers actively seek opportunities to breach business defences when individuals operate from home offices or off-site locations. Insufficient security protocols in remote desktop deployments open the door to malware, ransomware, or data theft. Lapses such as use of weak passwords or delays in applying necessary updates further raise the risk.

For organisations managing teams across different sites, maintaining a balance between data accessibility and strong protection is now essential. Secure remote access depends on layered measures that include strong authentication, full encryption, ongoing monitoring, and regular user security training. Companies adopting this strategy help sustain productivity and resilience while making severe security breaches less likely.

The Rising Security Risks of Remote Work Environments

The rapid shift to remote work has created increased opportunities for cybersecurity threats. Many businesses now support remote work, but a significant number lack proper security measures. Applying strong cyber hygiene and choosing to work securely with remote access software that builds in both data encryption and strong user authentication can close common gaps. This approach ensures that endpoints and connections benefit from layered protection, lowering risk from exploitable weaknesses.

Data breaches related to remote work vulnerabilities have become a growing concern for businesses, with costs and risks continuing to rise as more organizations adopt remote and hybrid work models.

Attackers frequently target Remote Desktop Protocol (RDP) connections. Common attack tactics include brute force password attempts, man-in-the-middle interceptions, and abusing unpatched RDP vulnerabilities. Traditional VPNs often fail to address all security risks in remote work settings because their protection usually ends at securing the tunneling of data between endpoints.

Essential Security Measures for Remote Desktop Access

Strong authentication forms the foundation of secure remote access. Multi-factor authentication (MFA) should be mandatory for all remote connections. This combines something users know (password), something they have (mobile device), and sometimes something they are (biometrics). MFA is widely recommended by security experts to reduce unauthorised access risks.

Data encryption protects information during transmission. All remote desktop connections should use end-to-end encryption with TLS 1.2 or higher because unencrypted sessions give attackers an opportunity to view or hijack sensitive data. Session management policies add another security layer. Organisations should implement automatic timeouts that disconnect idle sessions after a set period to prevent unauthorised access if users leave devices unattended.

IP filtering and geolocation restrictions limit connection sources. Companies can configure remote access systems to accept connections only from approved locations or IP ranges, blocking connection attempts from high-risk regions or unexpected places.

Implementing Zero Trust Architecture

Zero trust architecture requires companies to verify every user and device at every step, regardless of where the connection originates. For implementation, organisations should begin with segmenting their network so remote desktop users only access the exact resources needed for their tasks, and nothing more. Instead of full permissions, IT teams can assign application-level access and apply controls that check identities continuously, not just at login.

Administrators need to use strong authentication for every access attempt, apply end-to-end encryption for all remote sessions, and monitor user behaviour in real time. If any unusual activity is detected, such as a login from an unfamiliar location or attempts to access restricted files, system settings should automatically block the session and alert security staff.

Continuous verification is a key part of zero trust, unlike traditional perimeter-based security that validates users only at initial login. Remote desktop software with zero trust capabilities constantly monitors sessions for suspicious activities and can terminate connections if unusual patterns emerge. Using least privilege access is a practical step toward zero trust. Organisations should restrict user permissions to only what is necessary for their specific job functions.

READ ALSO: The Role of Scalable Hosting in Remote Work Environments

Data Protection Strategies Across Distributed Teams

Organisations must carefully plan their data storage methods for remote teams, because each approach creates different security and management needs. Centralised storage means all business data stays on company-controlled servers, making it easier for IT teams to enforce uniform security policies, back up files, and maintain compliance. 

However, securing remote access to these centralised resources becomes a major concern, as attackers may target remote desktop gateways or VPN connections searching for a single point of entry. Companies therefore need to implement identity verification, encrypted tunnels, and regular access audits to prevent breaches.

With distributed storage, some data is kept directly on remote user devices to speed up workflow and improve offline access. While this convenience allows staff to access documents without lag or connectivity barriers, it puts a heavier security burden on endpoint devices. Organisations need strict endpoint security protocols, including required encryption and automatic update enforcement.

Data classification systems help manage information security across locations. Companies should categorise data based on sensitivity (public, internal, confidential, restricted) and apply appropriate security controls to each level. This helps ensure the most sensitive information receives the strongest protections.

Creating an Effective Remote Work Security Policy

A strong remote work security policy needs several key components. These include device requirements, network security standards, authentication protocols, data handling rules, incident reporting procedures, and acceptable use guidelines. The policy should be clear, accessible, and regularly updated to address emerging threats.

Employee training is important for policy effectiveness. Each member of a remote workforce should regularly take part in security awareness training. This covers how to create strong passwords, ways to identify phishing attempts, methods for sharing files securely, and steps for setting up a home Wi-Fi network to reduce outside risks.

Incident response planning must cover remote access breaches. For this, companies should build clear procedures focused on remote work realities. Organisations should set up a central reporting channel like a dedicated email or hotline so staff can immediately flag suspicious activity including unauthorised logins or malware alerts.

Recovery plans should include actions like resetting passwords, restoring files from secure backups, and providing guidance to affected users about next steps. Clear communication protocols help everyone know what information must be shared and with whom. Regular practice drills help strengthen the process and support rapid, coordinated action during real threats.

Some industries are subject to additional legal and regulatory requirements regarding data protection and remote access. Organisations operating in these sectors should ensure their remote work solutions and policies align with relevant standards and undergo regular security assessments to identify and address potential vulnerabilities.

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Discover how AI models are revolutionizing cybersecurity workflows through automated threat detection, incident response, secure development, and intelligent security operations.

Cybersecurity has always been a field where speed matters a lot. That’s because attackers are fast and new vulnerabilities come up almost every day. The timeframe between a threat appearing and it causing damage is sometimes only a few minutes.

Over the years, security teams have been fighting this battle with tools and processes that weren’t built for the speed of modern threats. Now, AI models are offering a much-needed change. 

AI is transforming how cybersecurity works in threat detection, incident response, and vulnerability management. It’s not by replacing the humans already doing it. Instead, AI provides capabilities that weren’t possible before, and we’ll discuss how as you continue reading.

The Major Problem AI Is Solving in Cybersecurity

Before we go into why AI is having such a significant impact on cybersecurity workflows, let’s examine the problem. In one word, it’s volume.

Modern organisations generate large amounts of data, including network logs, authentication events, application behaviour, and more. Going through all that data manually to find information is not only difficult but more or less impossible at the speed threats demand.

Traditional security tools rely on rule-based systems. In other words, they use predefined conditions that trigger alerts when specific patterns are detected. These systems work well for known threats, but they struggle with newer attack methods. As a result, they often generate many false positives and require constant manual updating to stay relevant.

AI models fix the problem by learning from data rather than following fixed rules. They identify patterns, adjust to new behaviours, and improve over time. That’s why they’re a better fit for modern cyber threats.

Detecting Threat and Analysing Behaviours

An essential application of AI in cybersecurity is to spot threats early. Rather than looking for specific known attack patterns, AI models recognise normal behaviour for users, devices, and network traffic. Then, they flag significant differences once detected. This approach stops threats that rule-based systems miss entirely. 

Security teams using AI-powered behavioural analysis achieve two things. First, they report significant reductions in the volume of alerts they have to review. Second, they spend less time identifying threats, meaning more focus on real incidents.

Accessing AI Models for Cybersecurity Applications

Based on our research, a significant development in how organisations use AI for cybersecurity is the rise of flexible model platforms. Teams don’t have to build proprietary AI systems from scratch, which can be expensive and time-consuming. They can instead access powerful AI models through APIs. 

One of the best platforms for that is API QIK, which connects to many leading Large Language Models (LLMs). By providing a single access point to many AI systems, it makes a rather complex job much simpler. Plus, the platform helps reduce costs because you work with the best model per task.

For teams evaluating options, a unified AI model API platform is effective. It allows security engineers to test, compare, and deploy different models for various use cases. Such flexibility in a single platform significantly reduces the time from creating a concept to deploying it.

READ ALSO: What is the Digital Personal Data Protection Act and Who Does It Apply To?

Improving Incident Response

When an incident happens, how fast cybersecurity teams respond is crucial. The longer an attacker has access to a system, the more damage they can do. In this area, AI models are reducing the duration from detection to containment in ways that manual processes just can’t match.

With an AI-assisted incident response tool, it’s possible to correlate events on multiple systems. This way, teams can reconstruct an attack timeline, identify affected assets, and recommend actions to take. All this happens within seconds of a threat being detected.

To be clear, the automated process doesn’t remove the need for human judgment. At the end of the day, experienced analysts still make calls on how to respond. However, AI handles the time-consuming groundwork, so security professionals can focus on decision-making.

Large-Scale Vulnerability Management

Identifying and patching vulnerabilities before attackers can exploit them is one of the most important tasks in cybersecurity. Not to mention, it’s resource-intensive. Most organisations deal with more vulnerabilities at a time than they have the capacity to fix, so they’re forced to prioritise.

AI models help bring intelligence to this process. They don’t just list every known vulnerability by severity score. Instead, they can assess the actual risk each vulnerability poses, based on what the organisation does. As such, it’s a kind of contextual prioritisation that helps security teams channel resources toward the vulnerabilities that matter most.

AI Models and the Security Operations Centre

The Security Operations Centre (SOC) is where much of the day-to-day work of cybersecurity happens. You have analysts monitoring alerts, investigating suspicious activity, and responding to incidents. Basically, they work to stay ahead, so it’s demanding.

With a unified AI model marketplace in the SOC workflow, teams can skip the routine, repetitive tasks. We’re talking about actions such as log analysis, alert triage, and threat classification. The organisations making the most of this are those with a clear workflow for how AI outputs feed into human decision-making.

Human Review Remains Necessary

At this point, we want to be clear about something: AI is not replacing cybersecurity professionals. The industry threats are too complex and dependent on contextual judgment for that to happen.

What AI is doing is changing what cybersecurity professionals spend their time on. The systems allow less time sorting through noise or performing manual and repetitive investigation work. That leaves more room for strategic decisions, complex threat analysis, and the kind of creative thinking that attackers themselves use.

FAQ

Can AI replace cybersecurity professionals?

No. AI enhances productivity by automating repetitive tasks, but human expertise remains essential for investigation, decision-making, and strategic planning.

What cybersecurity tasks benefit most from AI?

Threat detection, log analysis, malware analysis, incident response, vulnerability assessment, secure coding, and threat intelligence processing are among the areas seeing the greatest impact.

Why use multiple AI models in cybersecurity?

Different AI models excel at different tasks. Combining them allows organizations to leverage the strengths of each model for more accurate and efficient workflows.

Bottom Line

The current trend of AI in cybersecurity isn’t temporary. From all indications, AI is becoming fundamental to how security work is done across the industry. It applies to detection, response, vulnerability management, and threat intelligence. 

Organisations that prioritise AI integration today will be better positioned to handle the threats of tomorrow. The good news is that the tools are more accessible than ever. Also, there are well-proven use cases, and the operational advantages are real. So, in conclusion, AI matters in cybersecurity, and more organisations need to build it into their workflows.

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In this post, I will talk about market research web scraping.

Market research has to do with gathering the right information at the ideal time. Nowadays, almost every business is data-driven, and web scraping is one of the most reliable ways to collect data at scale. You may want to track your competitors or monitor your customers’ preferences. Whichever it is, we’ll explain how web scraping can help as you continue reading. 

What is Market Research Web Scraping?

Simply put, market research web scraping involves extracting publicly available data for business purposes. As a business owner, your team won’t have to manually visit hundreds of web pages to copy information. Instead, you use scraping tools and scripts to automatically collect, structure, and analyze the data. It’s faster and much more efficient.

You can gather almost any type of market information using web scraping. That said, the common details most businesses are after include: 

• Competitor pricing 
• Product listings 
• Customer reviews 
• Social media mentions 
• Job postings
• News articles
• Industry trends 

If done properly, market research web scraping gets you detailed insights that would be impossible to do manually.

Reliable Proxies for Market Research Web Scraping

Choosing the right provider is critical because your entire data pipeline depends on speed, success rate, and resistance to blocking. Below are some of the best-performing options:

Oxylabs — High Success Rate & Enterprise Reliability

Oxylabs is widely recognized for delivering premium, enterprise-grade proxy infrastructure. With a 99% success rate, it’s built to handle demanding market research tasks where missing data is not an option.

It offers:

• Dedicated datacenter proxies for high-volume scraping
• One of the largest residential proxy networks globally
• Advanced tools like a Web Scraper API for automation

This makes Oxylabs especially effective for:

• Large-scale competitor price monitoring
• Aggregating product listings across multiple platforms
• Continuous data collection without interruptions

👉 If your focus is accuracy, uptime, and scale, Oxylabs is a top-tier choice.

Oxylabs Proxies

Oxylabs Proxies offer enterprise-grade, AI-powered proxy solutions with a massive 175M+ IP pool, ensuring unmatched...Show More

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Decodo — Smart Scraping with Built-In Automation

Decodo (formerly Smartproxy) is an award-winning proxy provider designed for efficiency and ease of use. Its standout feature is the dedicated Web Scraping API, which simplifies complex scraping workflows.

Key advantages include:

• Access to millions of IPs worldwide
• Automatic proxy rotation to avoid detection
• Built-in anti-blocking and CAPTCHA handling

Decodo is ideal for:

• Businesses that want plug-and-play scraping solutions
• Teams scaling data collection without heavy infrastructure
• Projects involving geo-targeted or restricted content

👉 If you want a balance of power, automation, and simplicity, Decodo delivers strong performance.

Decodo (formerly Smartproxy)

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Webshare — Scalable, Cost-Effective & Easy to Use

Webshare is a practical option for businesses that need reliable performance without excessive costs. With 500,000+ datacenter IPs and millions of residential proxies, it supports a wide range of market research use cases.

What makes Webshare stand out:

• Optimized for pricing intelligence and public data extraction
• Fast servers with dedicated bandwidth for consistent speed
• 24/7 expert customer support to assist with setup and scaling

It’s particularly useful for:

• Startups and growing data teams
• High-volume scraping with budget considerations
• Teams that want a simple, no-friction setup

👉 If your goal is affordable scalability with dependable support, Webshare is a solid choice.

Webshare Proxies

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Quick Recommendation Guide

5 Major Use Cases in Market Research

As we explained earlier, you can source various types of data using market research web scraping. Let’s now look at the five major ones:

1. Competitor Price Monitoring

Many businesses want to know what their competitors are charging, so they can adjust their prices accordingly. This is common, especially for service providers, where pricing is often private. Large-scale online businesses like e-commerce and travel platforms also use it to aggregate data from many sites. In such niches, web scraping allows you to extract and monitor costs in real time. 

2. Consumer Sentiment Analysis

After competitors, customers are the most important profiles to businesses. So, many companies use market research web scraping to source product reviews, forum discussions, and social media comments. That way, they know how customers feel about their products and services. It’s qualitative data that’s invaluable for marketing strategies and business development. 

3. Trend Identification 

If you’re in an industry and you don’t know what’s trending, your business won’t sell. To catch all the latest updates, you can use web scraping to retrieve data from news sites, niche blogs, and search engine results. The main advantage is that you’ll spot the trends early, before they become mainstream. As you adjust your strategy or product roadmap, you stay ahead of the competition.

4. Lead Generation and Prospecting 

Converting leads and prospects is one of the best signs of a successful business. Market research web scraping on directories, LinkedIn, and other industry sites can help you identify the right people to message. From the data you collect, you can build comprehensive prospect lists with business names, contact information, and company details.

5. Product and Inventory Research

This use case applies mostly if you’re a manufacturer or product retailer. Web scraping can be reliable in curating information from supplier catalogs and competitor product pages. Subsequently, you apply the data to track inventory levels and launch new, better products.

Proxies for Market Research Web Scraping

When scraping data for market research, it’s usually a large-scale project. Therefore, it’s necessary to get a reliable proxy to facilitate the process. The reason is that most websites set rate limits per IP address. So, if a site detects too many requests from a single IP, it automatically blocks that IP. Without proxies, then, data collection is slow, and the entire research operation may fail.

Thankfully, there are many secure proxies you can rely on for your market research. Here are our best picks: 

• Oxylabs: Offers high-quality proxies with a 99% success rate for retrieving public data. It has dedicated datacenter IPs for high-volume web scraping in the market research space.

• Decodo: An award-winning proxy service with a dedicated web scraping API. The API lets you scrape data with millions of IPs and built-in anti-blocking technology. 

• Webshare: Features 500,000+ datacenter IPs with specific use cases for pricing intelligence and public data. Expert customer support is available 24/7 to help you with your project. 

If you’re on a tighter budget, you can also consider IP Royal or Mars Proxies. Both have first-hand residential proxies that can be used for web scraping efficiently.

Bottom Line: Web Scraping Offers a Competitive Advantage

As a business, investing in web scraping for market research can easily put you ahead of your competitors. That’s because it gives you a broad view of the entire industry with data you can’t fetch through manual browsing or periodic reports. All you need is a proper scraping tool and a secure proxy like Oxylabs, Decodo, or Webshare.

You can use the Python-based Scrapy, which is highly customizable. Alternatively, you can go for Puppeteer or Playwright if the site is JavaScript-heavy and requires browser rendering. BeautifulSoup is also a decent option for lighter parsing tasks.

While scraping, it’s important to maintain ethical standards. What you’re after is mainly product or service information, pricing, reviews, and other publicly available business data. Define your targets, and avoid collecting personal information. Also, if the source website has an API, use it instead of scraping directly.

FAQs: Market Research Web Scraping

What is market research web scraping?

Market research web scraping is the process of automatically collecting publicly available data from websites to gain business insights. Instead of manually browsing hundreds of pages, scraping tools gather and structure data such as:

• Competitor pricing
• Product listings
• Customer reviews
• Social media mentions
• Industry trends

This allows businesses to make faster, data-driven decisions at scale.

How do businesses use web scraping for market research?

Businesses use web scraping across several key areas:

• Competitor monitoring → Track pricing and offerings
• Customer insights → Analyze reviews and sentiment
• Trend discovery → Identify emerging market opportunities
• Lead generation → Build targeted prospect lists
• Product research → Monitor inventory and supplier data

These insights help companies stay competitive and respond quickly to market changes.

Why are proxies important for market research scraping?

When scraping at scale, sending too many requests from a single IP can trigger rate limits or blocks. Proxies solve this by distributing requests across multiple IP addresses, ensuring smooth and uninterrupted data collection.

Top proxy providers for market research include:

• Oxylabs → High success rates and strong performance for large datasets
• Decodo → Advanced scraping API with built-in anti-blocking features
• Webshare → Reliable datacenter proxies with strong support and scalability

Using proxies ensures your scraping operation remains efficient and consistent.

4. Is market research web scraping legal?

Yes—if done correctly. Market research scraping should focus only on publicly available data such as pricing, product information, and reviews.

Best practices include:

• Avoid scraping personal or sensitive data
• Respect website terms of service
• Use official APIs where available

Following these guidelines helps ensure your data collection remains ethical and compliant.

5. What tools are best for market research web scraping?

The right tools depend on the complexity of your project:

• Scrapy → Powerful and customizable for large-scale scraping
• Puppeteer / Playwright → Ideal for JavaScript-heavy websites
• BeautifulSoup → Lightweight option for simple parsing tasks

To maximize performance, combine these tools with reliable proxies like Oxylabs, Decodo, or Webshare for scalable data extraction.

This combination allows you to build a robust, efficient market research system.

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In this post, I will talk about real-time data scraping.

If you do business, regardless of the kind, you’ll agree that market conditions shift by the minute. Also, consumer behavior is changing faster than ever. For these reasons, being able to collect and act on data in real time is a genuine competitive advantage.

Real-time data scraping involves continuously extracting live data from websites and online sources. In other words, you get information as it’s generated or updated, rather than collecting static details on a scheduled basis.

The good news is that real-time data scraping doesn’t have to be difficult. In this guide, we’ll explain how you can go about it effectively.

What Is Real-Time Data Scraping?

Traditional web scraping operates on a schedule. For a typical project, your script runs once a day, collects data, and stores it for later analysis. Real-time scraping is much more different. It has to do with steady monitoring of web sources and collecting data the moment it appears or changes.

The distinction matters a lot depending on the use case. For instance, a price change on a competitor’s website at 9:00 AM can be useful at 9:01 AM. However, if left until the following morning, it becomes almost irrelevant. Real-time scraping fixes that gap, providing current, immediately usable information.

Where Real-Time Data Scraping is Useful

We can point to several business areas where you can apply real-time data scraping for positive results. However, let’s consider the six major ones: 

1. Financial Market Data

Traders, analysts, and fintech platforms can use real-time scraping to monitor stock prices, cryptocurrency values, commodity rates, and financial news. In such an industry, even milliseconds of delay can result in huge trading differences. As such, up-to-date data is always a priority.

2. E-Commerce Price Tracking

Retailers and price comparison platforms regularly scrape product pages. They do so to detect price changes, flash sales, and stock availability updates the moment they happen. Many pricing engines feed directly from this real-time data to automatically adjust prices.

3. News and Media Monitoring

PR agencies, communications teams, and intelligence platforms need to track brand mentions and breaking stories as they develop. Scraping news outlets, blogs, and social media feeds in real-time makes that possible. 

4. Sports and Betting Data

We can’t debate that live sports scores, odds changes, and in-game statistics are among the most time-sensitive data types on the web. Real-time scraping powers the data feeds that betting platforms, DFS apps, and sports analytics tools depend on.

5. Travel and Hospitality

Here, the focus is on flight prices, hotel availability, and vacation rental rates. These details change frequently, and real-time scraping enables aggregators to obtain accurate pricing. They won’t have to deal with just cached results.

6. Cybersecurity Threat Intelligence

Security teams use real-time scraping to monitor dark web forums, paste sites, and threat intelligence feeds. That’s how they can quickly detect signs of data breaches, leaked credentials, and other cybercrimes.

Top Proxy Services for Real-Time Data Scraping

We’ve tested and evaluated several providers based on speed, reliability, scalability, and anti-detection performance. Below are some of the best proxy services for handling real-time scraping workloads:

Oxylabs — Enterprise-Level Speed & Real-Time Integration

Oxylabs is built for high-frequency, large-scale scraping environments where performance and uptime are critical. One of its standout features is the Realtime integration within its Web Scraper API, allowing you to extract data continuously without building complex infrastructure from scratch.

With a massive residential proxy pool, AI-powered scraping tools, and enterprise-grade SLAs, Oxylabs is ideal for:

• Financial data monitoring
• E-commerce price tracking at scale
• Mission-critical real-time pipelines

👉 If you’re running operations where every second matters, Oxylabs delivers consistency and precision.

Oxylabs Proxies

Oxylabs Proxies offer enterprise-grade, AI-powered proxy solutions with a massive 175M+ IP pool, ensuring unmatched...Show More

Oxylabs Proxies offer enterprise-grade, AI-powered proxy solutions with a massive 175M+ IP pool, ensuring unmatched reliability, speed, and anonymity for large-scale web scraping and data collection. Show Less

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Decodo — Smart Automation & Anti-Detection Strength

Decodo (formerly Smartproxy) is a strong choice for teams that want automation and flexibility without heavy engineering overhead. It features automatic proxy rotation, built-in CAPTCHA handling, and seamless Web Scraping API integration, making it easier to maintain stable real-time data flows.

Its infrastructure supports residential and mobile proxies, which are highly effective against modern anti-bot systems.

Best suited for:

• Geo-targeted scraping
• Platforms with strict bot detection
• Scalable real-time monitoring setups

👉 If your focus is staying undetected while scaling efficiently, Decodo is a reliable option.

Decodo (formerly Smartproxy)

Decodo (formerly Smartproxy) is an AI-powered proxy service and web scraping solutions provider that enables seamless...Show More

Decodo (formerly Smartproxy) is an AI-powered proxy service and web scraping solutions provider that enables seamless, large-scale data extraction with smart, reliable, and cost-effective tools for businesses of any size. Show Less

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Webshare — Fast, Scalable & Cost-Efficient

Webshare offers a practical balance of speed, affordability, and global coverage. With 500,000+ datacenter IPs and 80+ million residential proxies, it supports high concurrency and rapid request handling.

Its infrastructure is optimized for rate-limit avoidance and consistent throughput, which is essential for real-time scraping tasks that rely on frequent requests.

Ideal for:

• Startups and growing data operations
• High-volume scraping with budget constraints
• Projects needing simple, quick deployment

👉 If you want solid performance without overspending, Webshare is a strong contender.

Webshare Proxies

Webshare Proxies offers high-speed, customizable, and budget-friendly proxy solutions with flexible pricing, ensuring...Show More

Webshare Proxies offers high-speed, customizable, and budget-friendly proxy solutions with flexible pricing, ensuring seamless web scraping, automation, and online anonymity for businesses and individuals. Show Less

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IPRoyal — Flexible & Developer-Friendly

IPRoyal is known for its flexible pricing models and ease of integration, making it attractive for developers and smaller teams building real-time scraping systems. It offers residential, mobile, and datacenter proxies, with a focus on transparency and control.

While not as enterprise-heavy as Oxylabs, it performs well for:

• Mid-scale scraping projects
• Testing and development environments
• Businesses needing cost control with decent performance

👉 A good option if you want flexibility and simplicity without committing to enterprise pricing.

Mars Proxies — Emerging Option with Competitive Pricing

Mars Proxies is a newer player gaining attention for its competitive pricing and reliable proxy performance. It provides residential proxies with decent speed and uptime, making it suitable for light to moderate real-time scraping tasks.

It may not yet match the scale of top-tier providers, but it’s useful for:

• Budget-conscious scraping setups
• Smaller automation workflows
• Supplementing primary proxy networks

👉 Think of Mars Proxies as a supporting layer or entry-level solution for real-time scraping.

Quick Comparison Snapshot

Setting Up a Real-Time Scraping Operation

If you want to build a real-time scraping project, the setup is quite different from regular batch scraping. Based on our expertise, these are the most essential components you need:

Event-Driven Design

Real-time systems can’t run scrapers on a fixed schedule. Instead, they use an event-driven death, such that the scraper kicks off upon a trigger. It can be when a monitored page changes, a new listing appears, or some other condition is met. This approach minimizes wasted requests and guarantees fresh data.

Headless Browsers

Many modern websites are JavaScript-heavy and don’t render meaningful content in a basic HTTP request. Therefore, you’ll need headless browser tools like Playwright and Puppeteer. They can simulate full browser environments, allowing you to scrape content that loads automatically on a page.

WebSocket and API Monitoring

Some platforms deliver live data through WebSocket connections or public APIs. They skip the traditional HTTP pages. Such platforms are far more efficient than repeatedly requesting and parsing full web pages. However, you may need permission. 

Message Queues and Stream Processing

Depending on the scale, real-time scraping can generate enormous volumes of data. To process such large amounts of information without issues, you need tools like Apache Kafka and RabbitMQ. They handle high-throughput data streams, so the scraped data can be smoothly transferred into your storage and analytics systems.

Low-Latency Storage

Real-time data becomes useless if it can’t be stored and accessed fast. The best solutions here are in-memory databases like Redis and time-series databases like InfluxDB. As experts, we rate them as dependable for high-speed, time-stamped data that real-time scraping produces.

Function of Proxies in Real-Time Scraping

When you query the same sites continuously, you’ll often encounter IP-based rate limits and bot detection systems. So, for us, robust proxies are non-negotiable for real-time scraping. We’ve tested and ranked the top five services you can use below: 

• Oxylabs: This proxy service has a dedicated Realtime integration for its WebScraper API. You can use it to handle large-scale data extraction by the minute. 

• Decodo: This platform uses automatic proxy rotation and CAPTCHA handling for optimal real-time scraping. You can work with the Web Scraping API integration to make things easier. 

• Webshare: This proxy has 500,000+ datacenter IPs with fast servers and dedicated bandwidth. They have rate-limit avoidance that’s perfect for real-time scraping. 

If you want more proxy options, check out IP Royal or Mars Proxies. Each has dependable proxy IPs for real-time data scraping.

Bottom Line: Real-Time Data is a Strategic Asset

Mastering real-time data scraping helps you move faster and make better decisions as a business or agency. You may want to adjust prices the moment a competitor moves or capitalize on a market opportunity the instant it appears. Whichever it is, real-time information is invaluable.

As we explained, you’ll need the right technical tools to begin. Plus, a reliable proxy service built on providers like Oxylabs, Decodo, and Webshare is essential. That said, speed is only one angle for success. You also need to ensure quality by defining the right data fields and types.

FAQs: Real-Time Data Scraping

1. What is real-time data scraping and how is it different from traditional scraping?

Real-time data scraping involves continuously collecting data as it is created or updated, rather than pulling it at fixed intervals like traditional scraping.

The key difference is timing:

• Traditional scraping → Scheduled (e.g., once daily)
• Real-time scraping → Instant, event-driven

This matters because some data—like price changes or breaking news—loses value quickly if not captured immediately. Real-time scraping ensures you always work with fresh, actionable data.

2. What are the main use cases for real-time data scraping?

Real-time scraping is especially valuable in industries where timing is critical. Common use cases include:

• Financial markets → Tracking stock and crypto price movements
• E-commerce → Monitoring price changes and stock availability
• News & media → Detecting breaking stories and brand mentions
• Sports & betting → Capturing live scores and odds
• Travel → Updating flight and hotel pricing
• Cybersecurity → Monitoring threats and data leaks

In all these cases, even a small delay can reduce the usefulness of the data.

3. What technologies are required for real-time scraping?

A proper real-time scraping setup requires more advanced infrastructure than basic scraping. Key components include:

• Event-driven systems → Trigger scraping when changes occur
• Headless browsers → Handle JavaScript-heavy websites
• WebSocket/API monitoring → Capture live data streams
• Message queues (Kafka, RabbitMQ) → Process large data flows
• Low-latency databases (Redis, InfluxDB) → Store and access data instantly

These tools ensure your system can capture, process, and deliver data without delays.

4. Why are proxies essential for real-time data scraping?

Real-time scraping sends frequent requests to the same websites, which can quickly trigger rate limits, IP bans, or bot detection systems.

Proxies solve this by distributing requests across multiple IP addresses, ensuring continuous access. For reliable performance:

• Oxylabs → Offers real-time integration with its Web Scraper API for large-scale operations
• Decodo → Provides automatic proxy rotation and CAPTCHA handling
• Webshare → Delivers fast datacenter proxies with strong rate-limit avoidance

Using the right proxy provider ensures your scraping stays fast, stable, and uninterrupted.

5. What are the biggest challenges in real-time data scraping?

Real-time scraping is powerful, but it comes with challenges:

• High infrastructure demands
• Handling large volumes of streaming data
• Avoiding detection and blocks
• Maintaining low latency for storage and processing
• Ensuring data quality and accuracy

To succeed, you need a combination of strong architecture, efficient data pipelines, and reliable proxy services like Oxylabs, Decodo, or Webshare.

When done right, real-time scraping becomes a strategic advantage, helping you act faster than competitors.

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