The Ultimate Guide to Web Data Extraction

Web data extraction (also known as web scraping, web harvesting, screen scraping, etc.) is a technique for extracting huge amounts of data from websites on the internet. The data available on websites is generally not available to download easily and can only be accessed by using a web browser. However, web is the largest repository of open data and this data has been growing at exponential rates since the inception of internet.

Web data is of great use to Ecommerce portals, media companies, research firms, data scientists, government and can even help the healthcare industry with ongoing research and making predictions on the spread of diseases.

Consider the data available on classifieds sites, real estate portals, social networks, retail sites, and online shopping websites etc. being easily available in a structured format, ready to be analyzed. Most of these sites don’t provide the functionality to save their data to a local or cloud storage. Some sites provide APIs, but they typically come with restrictions and aren’t reliable enough. Although it’s technically possible to copy and paste data from a website to your local storage, this is inconvenient and out of question when it comes to practical use cases for businesses.

Web scraping helps you do this in an automated fashion and does it far more efficiently and accurately. A web scraping setup interacts with websites in a way similar to a web browser, but instead of displaying it on a screen, it saves the data to a storage system.

Applications of web data extraction

1. Pricing intelligence

Pricing intelligence is an application that’s gaining popularity by each passing day given the tightening of competition in the online space. E-commerce portals are always watching out for their competitors using web crawling to have real time pricing data from them and to fine tune their own catalogs with competitive pricing. This is done by deploying web crawlers that are programmed to pull product details like product name, price, variant and so on. This data is plugged into an automated system that assigns ideal prices for every product after analyzing the competitors’ prices.

Pricing intelligence is also used in cases where there is a need for consistency in pricing across different versions of the same portal. The capability of web crawling techniques to extract prices in real time makes such applications a reality.

2. Cataloging

Ecommerce portals typically have a huge number of product listings. It’s not easy to update and maintain such a big catalog. This is why many companies depend on web date extractions services for gathering data required to update their catalogs. This helps them discover new categories they haven’t been aware of or update existing catalogs with new product descriptions, images or videos.

3. Market research

Market research is incomplete unless the amount of data at your disposal is huge. Given the limitations of traditional methods of data acquisition and considering the volume of relevant data available on the web, web data extraction is by far the easiest way to gather data required for market research. The shift of businesses from brick and mortar stores to online spaces has also made web data a better 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 focused offering. Extracting this user generated content would be the first step in any sentiment analysis project and web scraping serves the purpose efficiently.

5. Competitor analysis

The possibility of monitoring competition was never this accessible until web scraping technologies came along. By deploying web spiders, it’s now easy to closely monitor the activities of your competitors like the promotions they’re running, social media activity, marketing strategies, press releases, catalogs etc. in order to have the upper hand in competition. Near real time crawls take it a level further and provides businesses with real time competitor data.

6. Content aggregation

Media websites need instant access to breaking news and other trending information on the web on a continuous basis. Being quick at reporting news is a deal breaker for these companies. Web crawling makes it possible to monitor or extract data from popular news portals, forums or similar sites for trending topics or keywords that you want to monitor. Low latency web crawling is used for this use case as the update speed should be very high.

7. Brand monitoring

Every brand now understands the importance of customer focus for business growth. It would be in their best interests to have a clean reputation for their brand if they want to survive in this competitive market. Most companies are now using web crawling solutions to monitor popular forums, reviews on ecommerce sites and social media platforms for mentions of their brand and product names. This in turn can help them stay updated to the voice of the customer and fix issues that could ruin brand reputation at the earliest. There’s no doubt about a customer-focused business going up in the growth graph.

Different approaches to web data extraction

There are businesses that function solely based on data, others use it for business intelligence, competitor analysis and market research among other countless use cases. However, extracting massive amounts of data from the web is still a major roadblock for many companies, more so because they are not going through the optimal route. Here is a detailed overview of different ways by which you can extract data from the web.

1. DaaS

Outsourcing your web data extraction project to a DaaS provider is by far the best way to extract data from the web. When depending on a data provider, you are completely relieved from the responsibility of crawler setup, maintenance and quality inspection of the data being extracted. Since DaaS companies would have the necessary expertise and infrastructure required for a smooth and seamless data extraction, you can avail their services at a much lower cost than what you’d incur by doing it yourself.

Providing the DaaS provider with your exact requirements is all you need to do and rest is assured. You would have to send across details like the data points, source websites, frequency of crawl, data format and delivery methods. With DaaS, you get the data exactly the way you want, and you can rather focus on utilizing the data to improve your business bottom lines, which should ideally be your priority. Since they are experienced in scraping and possess domain knowledge to get the data efficiently and at scale, going with a DaaS provider is the right option if your requirement is large and recurring.

One of the biggest benefits of outsourcing is the data quality assurance. Since the web is highly dynamic in nature, data extraction requires constant monitoring and maintenance to work smoothly. Web data extraction services tackle all these challenges and deliver noise-free data of high quality.

Another benefit of going with a data extraction service is the customization and flexibility. Since these services are meant for enterprises, the offering is completely customizable according to your specific requirements.

Pros:

• Completely customisable for your requirement
• Takes complete ownership of the process
• Quality checks to ensure high quality data
• Can handle dynamic and complicated websites
• More time to focus on your core business

Cons:

• Might have to enter into a long-term contract
• Slightly costlier than DIY tools

2. In house data extraction

You can go with in house data extraction if your company is technically rich. Web scraping is a technically niche process and demands a team of skilled programmers to code the crawler, deploy them on servers, debug, monitor and do the post processing of extracted data. Apart from a team, you would also need high end infrastructure to run the crawling jobs.

Maintaining the in-house crawling setup can be a bigger challenge than building it. Web crawlers tend to be very fragile. They break even with small changes or updates in the target websites. You would have to setup a monitoring system to know when something goes wrong with the crawling task, so that it can be fixed to avoid data loss. You will have to dedicate time and labour into the maintenance of the in-house crawling setup.

Apart from this, the complexity associated with building an in-house crawling setup would go up significantly if the number of websites you need to scrape is high or the target sites are using dynamic coding practices. An in-house crawling setup would also take a toll on the focus and dilute your results as web scraping itself is something that needs specialization. If you aren’t cautious, it could easily hog your resources and cause friction in your operational workflow.

Pros:

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

Cons:

• Maintenance of crawlers is a headache
• Increased cost
• Hiring, training and managing a team might be hectic
• Might hog on the company resources
• Could affect the core focus of the organisation
• Infrastructure is costly

3. Vertical specific solutions

There are data providers that cater to only a specific industry vertical. Vertical specific data extraction solutions are great if you could find one that’s catering to the domain you are targeting and covers all your necessary data points. The benefit of going with a vertical specific solution is the comprehensiveness of data that you would get. Since these solutions cater to only one specific domain, their expertise in that domain would be very high.

The schema of data sets you would get from vertical specific data extraction solutions are typically fixed and won’t be customizable. Your data project will be limited to the data points provided by such solutions, but this may or may not be a deal breaker depending on your requirements. These solutions typically give you datasets that are already extracted and is ready to use. A good example for a vertical specific data extraction solution is JobsPikr, which is a job listings data solution that extracts data directly from career pages of company websites from across the world.

Pros:

• Comprehensive data from the industry
• Faster access to data
• No need to handle the complicated aspects of extraction

Cons:

• Lack of customisation options
• Data is not exclusive

4. DIY data extraction tools

If you don’t have the budget for building an in-house crawling setup or outsourcing your data extraction process to a vendor, you are left with DIY tools. These tools are easy to learn and often provide a point and click interface to make data extraction simpler than you could ever imagine. These tools are an ideal choice if you are just starting out with no budgets for data acquisition. DIY web scraping tools are usually priced very low and some are even free to use.

However, there are serious downsides to using a DIY tool to extract data from the web. Since these tools wouldn’t be able to handle complex websites, they are very limited in terms of functionality, scale, and the efficiency of data extraction. Maintenance will also be a challenge with DIY tools as they are made in a rigid and less flexible manner. You will have to make sure that the tool is working and even make changes from time to time.

The only good side is that it doesn’t take much technical expertise to configure and use such tools, which might be right for you if you aren’t a technical person. Since the solution is readymade, you will also save the costs associated with building your own infrastructure for scraping. With the downsides apart, DIY tools can cater to simple and small scale data requirements.

Pros:

• Full control over the process
• Prebuilt solution
• You can avail support for the tools
• Easier to configure and use

Cons:

• They get outdated often
• More noise in the data
• Less customization options
• Learning curve can be high
• Interruption in data flow in case of structural changes

How web data extraction works

There are several different methods and technologies that can be used to build a crawler and extract data from the web.

1. The seed

A seed URL is where it all starts. A crawler would start its journey from the seed URL and start looking for the next URL in the data that’s fetched from the seed. If the crawler is programmed to traverse through the entire website, the seed URL would be same as the root of the domain. The seed URL is programmed into the crawler at the time of setup and would remain the same throughout the extraction process.

2. Setting directions

Once the crawler fetches the seed URL, it would have different options to proceed further. These options would be hyperlinks on the page that it just loaded by querying the seed URL. The second step is to program the crawler to identify and take different routes by itself from this point. At this point, the bot knows where to start and where to go from there.

3. Queueing

Now that the crawler knows how to get into the depths of a website and reach pages where the data to be extracted is, the next step is to compile all these destination pages to a repository that it can pick the URLs to crawl. Once this is complete, the crawler starts fetching the URLs from the repository. It saves these pages as HTML files on either a local or cloud based storage space. The final scraping happens at this repository of HTML files.

4. Data extraction

Now that the crawler has saved all the pages that needs to be scraped, it’s time to extract only the required data points from these pages. The schema used will be in accordance with your requirement. Now is the time to instruct the crawler to pick only the relevant data points from these HTML files and ignore the rest. The crawler can be taught to identify data points based on the HTML tags or class names associated with the data points.

6. Deduplication and cleansing

Deduplication is a process done on the extracted records to eliminate the chances of duplicates in the extracted data. This will require a separate system that can look for duplicate records and remove them to make the data concise. The data could also have noise in it, which needs to be cleaned too. Noise here refers to unwanted HTML tags or text that got scraped along with the relevant data.  

6. Structuring

Structuring is what makes the data compatible with databases and analytics systems by giving it a proper, machine readable syntax. This is the final process in data extraction and post this, the data is ready for delivery. With structuring done, the data is ready to be consumed either by importing it to a database or plugging it to an analytics system.

Best practices in web data extraction 

As a great tool for deriving powerful insights, web data extraction has become imperative for businesses in this competitive market. As is the case with most powerful things, web scraping must be used responsibly. Here is a compilation of the best practices that you must follow while scraping websites.

1. Respect the robots.txt

You should always check the Robots.txt file of a website you are planning to extract data from. Websites set rules on how bots should interact with the site in their robots.txt file. Some sites even block crawler access completely in their robots file. Extracting data from sites that disallow crawling is can lead to legal ramifications and should be avoided. Apart from outright blocking, every site would have set rules on good behavior on their site in the robots.txt. You are bound to follow these rules while extracting data from the target site.

2. Do not hit the servers too frequently

Web servers are susceptible to downtimes if the load is very high. Just like human users, bots can also add load to the website’s server. If the load exceeds a certain limit, the server might slow down or crash, rendering the website unresponsive for the users. This creates a bad user experience for the human visitors on the website which defies the whole purpose of that site. It should be noted that the human visitors are of higher priority for the website than bots. To avoid such issues, you should set your crawler to hit the target site with a reasonable interval and limit the number of parallel requests. This will give the website some breathing space, which it should indeed have.

3. Scrape during off peak hours

To make sure that the target website doesn’t slow down due to a high traffic from humans as well as bots, it is better to schedule your web crawling tasks to run in the off-peak hours. The off-peak hours of the site can be determined by the geo location of where the site’s majority of traffic is from. You can avoid possible overload on the website’s servers by scraping during off-peak hours. This will also have a positive effect on the speed of your data extraction process as the server would respond faster during this time.

4. Use the scraped data responsibly

Extracting data from the web has become an important business process. However, this doesn’t mean you own the data you extracted from a website on the internet. Publishing the data elsewhere without the consent of the website you are scraping can be considered unethical and you could be violating copyright laws. Using the data responsibly and in line with the target website’s policies is something you should practice while extracting data from the web.

Finding reliable sources

1. Avoid sites with too many Broken links

Links are like the connecting tissue of the internet. A website that has too many broken links is a bad choice for a web data extraction project. This is an indicator of the poor maintenance of the site and crawling such a site won’t be a good experience for you. For one, a scraping setup can come to a halt if it encounters a broken link during the fetching process. This would eventually tamper the data quality, which should be a deal breaker for anyone who’s serious about the data project. You are better off with a different source website that has similar data and better housekeeping.

2. Avoid sites with highly dynamic coding practices

This might not always be an option; however, it is better to avoid sites with complex and dynamic practices to have a stable crawling job running. Since dynamic sites tend to be difficult to extract data from and change very frequently, maintenance could become a huge bottleneck. It’s always better to find less complex sites when it comes to web crawling.

3. Quality and freshness of the Data

The quality and freshness of data must be one of your most important criteria while choosing sources for data extraction. The data that you acquire should be fresh and relevant to the current time-period for it to be of any use at all. Always look for sites that are updated frequently with fresh and relevant data when selecting sources for your data extraction project. You could check the last modified date on the site’s source code to get an idea of how fresh the data is.

Legal aspects of web crawling

Web data extraction is sometimes seen with clouded eye by people who aren’t very familiar with the concept. To clear the air, web scraping/crawling is not an unethical or illegal activity. The way a crawler bot fetches information from a website is in no different from a human visitor consuming the content on a webpage. Google search, for example runs of web crawling and we don’t see anyone accusing Google of doing something even remotely illegal. However, there are some ground rules you should follow while scraping websites. If you follow these rules and operate as a good bot on the internet, you aren’t doing anything illegal. Here are the rules to follow:

1.   Respect the robots.txt file of the target site
2.   Make sure you are staying compliant to the TOS page
3.   Do not reproduce the data elsewhere, online or offline without prior permission from the site

If you follow these rules while crawling a website, you are completely in the safe zone.

Conclusion

We covered the importance aspects of web data extraction here like the different routes you can take to web data, best practices, various business applications and the legal aspects of the process. As the business world is rapidly moving towards a data-centric operational model, it’s high time to evaluate your data requirements and get started with extracting relevant data from the web to improve your business efficiency and boost the revenues. This guide should help you get going in case you get stuck during the journey.