My Website Parser Broke – How Do I Fix It?

In an era of increasing digitalization, data is the new currency. It is one of many factors that determine whether you can keep pace with your competitors. The more data one has, the greater the advantage. One way to obtain data is through parsing.

Web Data Parsing for Business

Parsing is the process by which data is extracted from websites. Bots used for data extraction are called crawlers or spiders. This is not pixel-by-pixel extraction, but rather extraction of the underlying HTML code and the data it contains.

Many companies rely on parsing to obtain data – from market research firms that use social media data for sentiment analysis, to websites that automatically adjust prices for seller sites.

Web Data Parsing Techniques

Manual Parsing

Manual parsing involves copying and pasting relevant information and creating a spreadsheet to track data. As simple as manual copying may sound, it has its pros and cons:

Pros

•     One of the simplest parsing methods, it requires no prior knowledge or skills in using a web data crawler.
•     There is little room for error since human oversight is possible during the data extraction process.
•     A common issue with parsing processes is that rapid data extraction often leads to being blocked from the site. Since manual collection is slow, blocking is not a concern.

Cons

•     Slow speed is also a hindrance to time management. Bots are significantly faster at parsing than humans.

Automated Parsing

Automated parsing, or web data crawling, can be done by writing code and creating your own DIY scraping engine, or by using subscription-based tools that your business team can manage with a week of training. Numerous no-code tools have become popular over time because they are easy to use and save time and money.

For those who want to build their own crawlers or scrapers for web data collection, you can hire a team to develop the code needed to collect data from multiple web pages, then automate the entire process by deploying scripts with this information in the cloud. The processes involved in automated parsing typically include one or more of the following:

HTML Parsing: HTML parsing uses JavaScript and is applied to linear or nested HTML pages. It is commonly used for extracting links, screen capture, text extraction, resource extraction, etc.

DOM Parsing: The Document Object Model, or DOM, is used to understand the style, structure, and content of XML files. DOM parsers are used when the scraper wants a deep understanding of the web page structure. A DOM parser can be used to find nodes that carry information, and then tools like XPath can be used to parse web pages. Web browsers such as Internet Explorer or Mozilla Firefox can be used together with specific plugins to extract relevant data from web pages, even if the generated content is dynamic.

Vertical Aggregation: Vertical aggregation platforms are created by companies that have access to large-scale computing power to work with specific verticals. Sometimes companies use the cloud to run these platforms. Bots are created and monitored by the platforms without human intervention, based on a vertical knowledge base. For this reason, the effectiveness of the created bots depends on the quality of the data they extract.

XPath: XML Path Language, or XPath, is a query language used to work with XML documents. Since XML documents have a tree structure, XPath is used for navigation by selecting nodes based on various parameters. XPath together with DOM parsing can be used to extract entire web pages.

Google Sheets: Google Sheets is a popular choice for parsers. In Sheets, the IMPORTXML(,) function can be used to collect data from websites. This is especially useful when the parser wants to extract specific data or patterns from a website. The command can also be used to check whether your site is protected against scraping.

Text Pattern Matching: This is a common expression matching technique that uses the UNIX grep command and is typically included in programming languages such as Perl or Python.

Such parsing tools and services are widely available online, and customers themselves do not need to be highly skilled in the aforementioned techniques if they do not want to parse data themselves. Tools like CURL, Wget, HTTrack, Import.io, Node.js, and others are largely automated. Automated headless browsers such as Phantom.js, Slimer.js, Casper.js can also be used in parsing.

Pros

•     Automated web data parsing helps you extract hundreds of data points from thousands of web pages in seconds.
•     Tools are easy to use. Even a non-programmer or hobbyist can use a user-friendly interface to collect data from the web.
•     Some tools can be configured to run on a schedule and then deliver the extracted data as a Google Sheet or JSON file.
•     Most languages, such as Python, come with special libraries like BeautifulSoup that help easily parse data from the web.

Cons

•     Tools require training, and DIY solutions require expertise – so you need to either dedicate part of your business team's energy to parsing, or hire a technical team to handle custom web scraping.
•     Most tools come with some limitations; one may not help you collect data behind a login screen, while others may have issues with embedded content.
•     For paid no-code tools, you can request updates, but fixes may be slow and could be useless when facing tight deadlines.

Data as a Service (or DaaS)

As the name suggests, this means outsourcing the entire data extraction process. Your infrastructure, code, maintenance – everything will be taken care of. You provide the requirements and receive the result.

The parsing process is complex and requires skilled developers. The infrastructure and labor resources needed to maintain your own data parsing system can become too burdensome, especially for companies that do not yet have their own technical team. In such cases, it is better to use an external custom parsing service.

Using DaaS has many advantages; here are a few:

Focus on Core Business

Instead of spending time and effort on the technical aspects of parsing and building an entire team around it, outsourcing allows you to focus on your core business.

Cost-Effectiveness Compared to DIY Web Data Collection

An in-house parsing solution will be more expensive than DaaS services. Parsing is not simple work, and complexity means you will need to hire skilled developers, which will ultimately cost a lot. Since most DaaS solutions charge only for usage, you pay only for the data extracted and the overall data volume.

No Maintenance

When you build your own solution or use parsing tools, there is an additional risk that the bot might break due to changes on websites or other technical issues that may need immediate fixing.

This could mean that someone or an entire team must constantly monitor for inaccuracies in the collected data and manage overall system downtime. Since websites can change frequently, the code must be updated every time, or there is a risk of failure. With DaaS providers, you do not have to bear the extra hassle of maintaining your own parsing solution.

When it comes to web data parsing, you can choose from the methods above according to your specific needs. However, if you require an enterprise-grade solution, at ESK Solutions we offer a fully managed service that can provide you with cleaned and formatted data points according to your preferences.

You only need to specify your requirements, and we will provide you with the data that you can plug in and use. By using our solution, you can forget about the inconveniences of maintenance, infrastructure, time and costs, as well as blocking during data collection from the site.