The 4 Challenges of Data Scraping and How to Overcome Them

Do you want to scrape content from a website but are unsure how to go about it? Data scraping, which used to be relatively straightforward to accomplish, has become increasingly challenging and difficult to scale.
5 min read
The 4 Challenges of Data Scraping and How To Overcome Them

You will learn about the pros and cons of the different routes in this article, and how to gather data the fastest and most efficiently. 

Extracting data from a website presents four main challenges:

Challenge No. 1: Software 

Use a third-party vendor or build your own software infrastructure? 

Do-it-Yourself (DIY)

To create a data scraper, you can hire software developers to write proprietary code. There are multiple open-source Python packages available, for example: 

  • BeautifulSoup
  • Scrapy
  • Selenium

The benefit of proprietary coding is that the software is tailored to your current needs. However, the cost is high:

  • Hundreds or thousands of hours of coding
  • Software and hardware purchases and licenses
  • The proxy infrastructure and bandwidth will still cost you, and you will still have to pay even if the collection failed

Software maintenance is one of the biggest challenges. When the target website changes its page structure, which happens very frequently, the crawler breaks, and the code needs to be repaired. 

And you’ll still need to overcome the other three challenges listed below. 

Data Scraping Tools

You may also use a third-party vendor such as Bright Data, specializing in this area.

Other software available on the internet may be old and outdated. Caveat emptor – buyer beware. If the website looks like it was created in the previous century, that may reflect on their software.   

Bright Data has a no-code platform called Web Scraper IDE that does all the data extraction, and you only pay for success. See below for more information.

Challenge No. 2: Blocking

Isn’t it frustrating trying to access a website only to be challenged with a puzzle to prove we are not robots? The irony is that the puzzle challenge is a robot!

Getting past the bots is not just a problem when trying to access a website. To extract data from public websites, you’ll have to get past the robots standing guard at the gates. CAPTCHAs and ‘site sentries’ attempt to prevent bulk data collection. It’s a game of cat and mouse where the technical difficulty increases with time. Stepping carefully and successfully through the minefield is Bright Data’s specialty.  

Challenge No. 3: Speed & Scale

Both speed and scale of data scraping are related challenges that are influenced by the underlying proxy infrastructure: 

  • Many data scraping projects begin with tens of thousands of pages but quickly scale to millions 
  • Most data scraping tools have slow collection speeds and limited simultaneous requests per second. Make sure you check the vendor’s collection speed, factor in the number of pages needed, and consider the collection frequency. If you only need to scrape a small number of pages and you can schedule the collection to run at night, then this may not be an issue for you  

Challenge No. 4: Data Accuracy 

Our previous discussion addressed why some software solutions may not be able to retrieve data at all or with partial success. Changes to the site’s page structure may break the crawler/data collector, causing the data to be incomplete or inaccurate. 

In addition to the accuracy and completeness of the dataset, check how the data will be delivered and in what format. The data must be integrated seamlessly into your existing systems. By tailoring your database schema, you can expedite the ETL process.  

Bright Data’s Solution

Bright Data’s newly developed platform, Web Scraper IDE, addresses these challenges.

It is a no-code, all-in-one solution that combines:

  • Bright Data’s residential proxy network and session management capabilities
  • Proprietary website unlocking technology
  • Advanced data collection and restructuring

The structured data is provided in CSV, Microsoft Excel, or JSON format, can be sent via email, webhook, API, or SFTP, and stored on any cloud storage platform. 

Who needs web data?

Who doesn’t? Below are just a few examples:

  • With Web Scraper IDE, eCommerce companies can compare their products and prices with those of their competitors, such as Amazon, Walmart, Target, Flipkart, and AliExpress
  • Business owners are scraping social media sites such as Instagram, TikTok, YouTube, and LinkedIn for lead enrichment or to find top influencers
  • Real-estate companies compile a database of listings in their target markets

Putting it all together

If you want to extract web data, you’ll want to consider:

  • Development/maintenance of your own solution versus using a third-party solution
  • What kind of proxy network does the company offer? Are they reliant on third-party vendors such as Bright Data for their infrastructure? How reliable is their network?
  • The software’s ability to overcome site obstacles and retrieve the required web data. What success rate can you expect? Does the bandwidth charge depend on whether a collection is successful or not? 
  • Does the company comply with data privacy laws? 

Additionally, consider whether you want a solution that includes:

  • Best-of-breed proxy network access
  • Maintenance of your web crawlers/data collectors
  • An account manager to take care of your day-to-day operations and business needs
  • 24×7 technical support  

数据采集中的四大障碍和解决方法

你想采集网页数据却无从下手?过去相对容易的数据抓取在信息爆炸的今天变得越来越具有挑战性,特别是在大批量数据抓取时。
1 min read
The 4 Challenges of Data Scraping and How To Overcome Them

这篇文章我们将会讨论各种数据抓取的优势和劣势,以及如何才能快捷大批量进行数据抓取。

网页数据抓取时所面临的四大挑战:

挑战一:软件

自建还是直接使用外包软件?

自建

要创建数据抓取工具,您可以聘请软件开发人员编写专有代码,以下开源Python 包都可以使用:

  • BeautifulSoup
  • Scrapy
  • Selenium

自建的优点是该软件完全根据你的需求量身定制,缺点是成本很高,你需要

  • 数百或数千个小时的编码;
  • 软件和硬件购买许可;
  • 代理的基础设施以及宽带费用,即使采集失败,你也需要支付;

软件维护极具挑战:目标网站经常更改页面结构,导致爬虫崩溃,工程师需要修复代码。

除了这些烦恼,你还需要面对以下挑战。

数据抓取工具

你可以使用专门从事该领域的第三方供应商,比如亮数据Bright Data。

市面上有很多数据采集器没有及时更新,-甚至从其网页都能看得出来。

亮数据有一个名为 数据采集器的平台,实现零代码数据自由提取,且只需要为成功的采集任务付费。

挑战二:反爬取技术

试图访问网站时却被频繁要求输入验证码来证明我们不是机器上是不是很气馁?好笑的是,这种验证码本身就是机器人!

在爬取网站数据时,绕过机器人并不是唯一的难题,要提取网站数据信息你还需要绕过很多机器人,验证码和“站点哨兵”总是试图阻止批量数据收集。 这是一场猫捉老鼠的游戏,时间越长,技术难度越高。 谨慎而成功地通过雷区是亮数据的专长。

挑战三:速度和规模

不管是代理网络的速度还是规模都和代理基础设施是否强大有十分密切的关系。

  • 很多数据抓取项目从数万页开始,然后很快扩展到数百万页。
  • 市面上绝大部分数据抓取工具的速度较慢,每秒发送请求有限。如果只是需要抓取少量页面数据,并可以在网速相对会比较快的时段(比如深夜)进行,那应该问题不大。但是,如果是企业的大规模抓取,考虑到采集频率等因素,考察供应商的基建设施是否够强大就十分必要。

挑战四:数据的准确性

如前所述,有些软件的解决方案可能无法顺利抓取数据,或只能部分成功,因为网站的页面结构更改会破坏爬虫工具或数据采集工具,导致数据的不完整或者不正确。

除了完整性和正确性,还需要看数据存储格式和交付方式是否能满足需求,数据能否无缝集成到你的现有系统,通过定制您的数据库模式,您可以加快数据 ETL 过程。

亮数据的解决方案

亮数据的自动数据采集平台提供了完美地解决这些问题的方案。

最重要的是,零代码需求。

  • 真人住宅代理网络和会话管理功能
  • 专有的网站解屏解锁功能
  • 升级的数据采集和重组

结构化数据以 CSV、Microsoft Excel 或 JSON 格式提供,可以通过电子邮件、Webhook、API 或 SFTP 发送,并存储在任何云存储平台上。

你需要亮数据吗?

只要你采集网页数据,亮数据就是你的首选!以下为几个例子:

  • 借助数据采集器,电商可以全面了解竞品的价格和产品,与之比较,做出最好的定价策略。这些平台包括 亚马逊Amazon, 沃尔玛Walmart, Target, Flipkart, 速卖通等等;
  • 企业主通过抓取社交媒体 Ins, TikTok, 脸书和 领英LinkedIn 等社交媒体网站信息,开发潜在客户或定位顶级网络红人;
  • 房地产公司编制一个目标市场的数据库。

小结

如果你需要采集网页数据信息,需要考虑

  • 开发和维护自己的基建设施和解决方案还是使用第三方专业供应商?
  • 供应商的代理网络情况如何?是依靠供应商提供基建设施还是像亮数据一样有自己的基建设施?网络速度和稳定度可不可靠?特别是在大规模高频率采集需求情况下。
  • 软件能够克服网站障碍并检索所需的网络数据? 你能期待什么样的成功率? 带宽费用是否取决于收集是否成功?
  • 供应商是否遵循相关数据隐私法案?

其它需要考虑的额外因素包括:

  • 行业类是否被强烈推荐,且获得最佳称号;
  • 是否会维护你的爬虫网络/数据采集器;
  • 专属客户经理,快速专业地负责你的日常运营和业务需求;
  • 24×7技术支持。