Complete Guide to Web Data Extraction

Extracting data from the Internet (also known as parsing, web scraping) is a technique for extracting vast amounts of data from websites. The data available on websites cannot be easily downloaded and is only accessible via a web browser. However, the Internet is the largest repository of open data, and this data has been growing exponentially since the dawn of the Internet.

Web data is extremely useful for e-commerce portals, media companies, research firms, data scientists, governments, and can even help healthcare conduct research and forecast disease spread.

Consider that data available on classified ad sites, real estate portals, social networks, retail sites, online stores, etc., is easily accessible in a structured format and ready for analysis. Most of these sites do not provide functionality to save data to local or cloud storage.

Some sites provide APIs, but they typically have limitations and are not reliable enough. While it is technically possible to copy and paste data from a site into local storage, it is inconvenient and impractical for real business use.

Web scraping does this automatically and does it much more efficiently and accurately. Scraping software interacts with websites like a web browser, but instead of displaying data on the screen, it saves it to a database.

Areas of Application for Web Data Scraping

1. Price Analytics

Price analysis is a trend that is gaining popularity every day, given the intensifying competition in the online space. E-commerce portals constantly monitor their competitors using web intelligence to obtain price data from them in real time and adjust their own catalogs with competitive prices. To do this, they use scrapers programmed to obtain detailed product information: product name, price, variant, and so on.

This data is fed into an automated system that assigns ideal prices for each product after analyzing competitor prices.

Price analytics is also used when it is necessary to ensure price consistency across different versions of the same portal. The ability of web scraping methods to extract prices in real time makes such applications a reality.

2. Cataloging

E-commerce portals typically contain a huge number of product listings. Updating and maintaining such a large catalog is not easy.

Therefore, many companies turn to web data scraping services to collect the data needed to update catalogs. This helps them discover new categories they were unaware of or update existing catalogs with new product descriptions, images, or videos.

3. Market Research

Market research is incomplete if you do not have a huge amount of data at your disposal. Given the limitations of traditional data collection methods and the volume of relevant data available on the Internet, web data extraction is by far the easiest way to gather the data needed for market research.

The shift of businesses from brick-and-mortar stores to the online space has also made web data the best resource for market research.

4. Sentiment Analysis

Sentiment analysis requires data extracted from websites where people share their reviews, opinions, or complaints about services, products, movies, music, or any other consumer-oriented offerings.

Scraping such user-generated content is the first step in any sentiment analysis project, and it effectively accomplishes this task.

5. Competitor Analysis

The ability to monitor competitors has never been as accessible as it is now with the advent of parsing technology. With web crawlers, it's now easy to track competitors' activities, such as their advertising campaigns, social media activity, marketing strategies, press releases, catalogs, etc., to gain a competitive edge. Near-real-time parsing allows companies to obtain competitor data in real time.

6. Content Aggregation

Media websites require instant access to the latest news and other relevant information on the Internet. Timeliness in news delivery is a crucial factor for these companies. Parsing allows them to monitor or extract data from popular news portals, forums, or similar sites for trending topics or keywords they wish to track. Low-latency parsing is used in this case because the update speed needs to be very high.

7. Brand Monitoring

Every brand today understands how important customer focus is for business growth. It is in their interest to have a clean brand reputation if they want to survive in this competitive market. Most companies now use solutions to monitor popular forums, reviews on e-commerce sites, and social media platforms for mentions of their brand and product name.

This, in turn, helps them stay on top of customer sentiment and address issues that could tarnish the brand's reputation at the earliest stages. There is no doubt that a customer-oriented business climbs the growth chart.

Different Approaches to Web Data Extraction

Some enterprises operate exclusively on data, while others use it for business intelligence, competitive analysis, market research, etc., among countless other use cases.

However, extracting huge volumes of data from the web remains a significant hurdle for many companies, largely because they do not take the optimal path. Below is a detailed overview of the different ways to extract data from the web.

1. DaaS

Outsourcing your web data extraction project to a DaaS provider is undoubtedly the best way to extract data from the web. When using a data provider's services, you are completely relieved of the responsibility for setting up, maintaining, and ensuring the quality of the extracted data.

Since DaaS companies have the expertise and infrastructure necessary for seamless and smooth data extraction, you can use their services at a much lower cost than if you were to do it yourself.

Providing the DaaS provider with your exact requirements is all you need to do, and the rest is taken care of. You'll need to communicate details such as data points, source websites, crawl frequency, data format, and delivery methods. With DaaS, you get the data exactly as you want it and can focus on using it to improve your business metrics, which should ideally be your priority.

Since they have parsing expertise and knowledge to efficiently and scalably obtain data, turning to a DaaS provider is the right option if your needs are large and recurring.

One of the biggest advantages of outsourcing is data quality assurance. Since the web is inherently very dynamic, data extraction requires constant monitoring and maintenance for smooth operation.

Data parsing services

These services solve all these problems and provide high-quality data without interruptions.

Another advantage of data extraction services is flexibility and customization. Since these services are designed for enterprises, the offering is fully customizable to your specific requirements.

Pros:

• Fully customizable to your requirements

Cons:

• May require a long-term contract
• Slightly more expensive than DIY tools

2. In-House Data Extraction

If your company is technically well-equipped, you can opt for in-house data extraction. Web scraping is a technically complex process that requires a team of skilled programmers to develop scraper scripts, deploy them on servers, debug, monitor, and post-process the extracted data. In addition to the team, you will also need high-end infrastructure to run scraping tasks.

Maintaining your own scraping system can be more challenging than building it. Web scrapers are generally very fragile. They break even with minor changes or updates to target websites.

You'll need to set up a monitoring system to know when something goes wrong with a scraping task, so it can be fixed to avoid data loss. You'll have to spend time and effort maintaining the in-house scraping system.

Moreover, the complexity involved in building your own scraping system increases significantly if the number of websites to scrape is large or if the target sites use dynamic coding methods. An in-house scraping system will also divert attention and dilute results, since web scraping itself requires specialization.

If you're not careful, it can easily consume all your resources and cause friction in your workflow.

Pros:

• Full ownership and control over the process
• Ideal for simpler requirements

Cons:

• Maintaining scrapers is a headache
• Increased costs
• Hiring, training, and managing a team can be difficult
• Can drain company resources
• Can affect the organization's core focus
• Infrastructure is costly

3. Vertical-Specific Solutions

Some data providers serve only a specific industry vertical. Vertical-specific data extraction solutions are a great option if you can find a solution that serves the area you're targeting and covers all the data points you need. The advantage of a vertical-focused solution is the completeness of the data you'll receive. Since such solutions serve only one specific area, their expertise in that area will be very high.

The schema of the datasets you receive from vertical data extraction solutions is typically fixed and not customizable. Your data project will be limited to the data points provided by such solutions, but this may or may not be a deciding factor depending on your requirements.

Such solutions usually provide you with already extracted and ready-to-use datasets. A good example of a vertical-specific data extraction solution is JobsPikr — a solution for extracting data from job listings that pulls data directly from company career pages worldwide.

Pros:

• Comprehensive industry data
• Fast access to data
• No need to deal with complex data extraction aspects

Cons:

• Lack of customization options
• Data is not exclusive

4. DIY Data Extraction Tools

If you don't have the budget to build your own scraping system or outsource data extraction to a provider, you're left with DIY tools. These tools are easy to learn and often provide a point-and-click interface to make data extraction easier than you might imagine.

These tools are an ideal choice if you're just starting out and don't have a budget for data collection. DIY scraping tools usually have a very low price, and some are even free to use.

However, using DIY tools for web data extraction has serious drawbacks.

Since these tools can't handle complex websites, they are very limited in terms of functionality, scale, and data extraction efficiency. Maintaining DIY tools will also be a challenge because they aren't built flexibly.

You'll have to monitor the tool to ensure it's working and even make changes from time to time.

The only advantage is that setting up and using such tools doesn't require extensive technical knowledge, which may suit you if you're not a technical person. Since the solution is ready-made, you also save on costs associated with building your own scraping infrastructure. As for the drawbacks, DIY tools can meet the needs for simple and small-scale data.

Pros:

• Full control over the process
• Ready-made solution
• You can get tool support
• Easier to set up and use

Cons:

• They often become outdated
• More data noise
• Fewer customization options
• Learning curve can be high
• Data flow disruption in case of structural changes

How Web Data Scraping Works

Several different methods and technologies can be used to create a scraper and extract data from the web.

1. Seeding

The seed URL is where it all begins. The scraper will start its journey from that URL and begin looking for the next URL in the data retrieved from that link. If the scraper is programmed to crawl the entire site, the initial URL will match the domain root.

The seed URL is programmed into the scraper during setup and remains unchanged throughout the extraction process.

2. Collection Directions and Their Settings

Once the scraper receives the main URL, it has various options for what to do next. These options will be the hyperlinks on the page it just loaded by requesting the initial URL.

The second step is to program the scraper to autonomously determine and traverse different routes from that point. At this stage, the bot knows where to start and where to go next.

3. Queueing

Now that the scraper knows how to delve into the depths of the site and reach the pages where the data to be extracted resides, the next step is to gather all those target pages into a repository from which it can select URLs for scraping. After that, the scraper retrieves URLs from the repository. It saves these pages as HTML files in local or cloud storage. The final scraping occurs in this repository of HTML files.

4. Data Extraction

Now that the scraper has saved all the pages to be collected, it's time to extract only the necessary data from them. The schema used will match your requirements. Now is the time to instruct the scraper to select only the needed data points from these HTML files and ignore the rest. You can teach the scraper to identify data points based on HTML tags or class names associated with the data points.

5. Deduplication and Cleaning

Deduplication is a process performed on extracted records to eliminate the likelihood of duplicates in the extracted data. This requires a separate system that can search for duplicate records and remove them to make the data concise. Noise may also be present in the data, which also needs to be cleaned.

By noise here we mean unnecessary HTML tags or text that was collected along with the relevant data.  

6. Structuring

Structuring is what makes data compatible with databases and analytical systems by giving it proper, machine-readable syntax. This is the final data extraction process, after which the data is ready for transfer.

After structuring, the data is ready for use either by importing into a database or by connecting to an analytical system.

Best Practices for Web Data Extraction 

As an excellent tool for gaining powerful insights, web data extraction has become a must for businesses in this competitive market. As with the most powerful things, scraping must be used responsibly. Here are some best practices you should follow when scraping data from websites.

1. Respect robots.txt

You should always check the robots.txt file