Web Scraping for Big Data

Information is the most valuable commodity there is, since those who possess the information rule the world. In the digital era we live in, Big Data sets are the cornerstones of data science, Big Data analytics, Machine Learning, and tutoring the algorithms of Artificial Intelligence. All these technologies require extensive data scraping from various websites.

Scraping for big data is the process of web crawling and collecting target data from different web sources at a large scale. The term “big data” has a lot of meanings, but here we mean datasets that contain more than 10 million records. Large scale web scraping requires more advanced technologies and approaches. From our experience, our clients use big data scraping in two ways: for analysis and for machine learning tasks.

Below, we’ve described the most common differences between normal data scraping and big data scraping.

When you try to gather data from one website many times, you might be blocked from scraping it with anti scraping technologies protecting the site. Some websites have limitations on the number of requests at a particular time or from a particular location. In that case, you need to use proxy servers — remote computers with different IP addresses. This helps to create the illusion that different users are trying to access the targeted web source. If you’d like more information on using proxies, check out this topic on our blog, where it is covered in detail.

Depending on how many web sources you want to scrape, you may need to use a web crawling system. This helps you to visit all the web sources you need and scrape them for relevant information. All this needs to be managed by special crawling software. This software will decide which web sources should be visited, when they should be visited, and from which location. The software will set special rules for web scrapers and web parsers — relatively simple software that you can just copy and operate with extracted information.

These systems allow you to manage and store scraped data. Big data needs equally big storage. You can scrape images, text, or other files — each data type requires its own data storage and data management systems. Big data web scraping should be carried out with the desired business goals specified and the correct data sources identified in advance. After gathering the relevant information and cleansing it, users or data scientists can analyze it for insights or process it further. Let’s look into this in more detail.

A great amount of data scraping and analytics is aimed at the existing workflow improvement, brand awareness, and market impact enhancement, or providing cutting-edge customer service and experience. To achieve these aims you should:

Your goals should be specific and tangible, you should have a clear picture of what you want and what you must do to achieve it. You can, for instance, set a goal to increase sales and try to figure out what product your target customers prefer through the analysis of your clients’ feedback to surveys, their activity on social media, and various review platforms. With the received insights in mind, you can then alter your product mix accordingly.

To guarantee credible results, extract data from relevant web pages and sources. It’s also vital to check the target websites for the credibility of their data.

Before analyzing the received data set, make sure it covers all the essential metrics and characteristics from at least one relevant source. When this is done, a proper Machine Learning algorithm should be applied to provide the expected outcomes.

On receiving the Big Data analysis results, you should take action based on these results to reach the business goals you have set. Having a certain product in stock in abundance, for instance, or by considering a relevant promo or giveaway. It’s vital to act while your Big Data analysis results are still current, or you risk having gone through the whole process in vain.

To check the effectiveness of your decisions and actions grounded on Big Data mining analysis, set certain KPIs (Key Performance Indicators) — the level of sales growth, a decrease in marketing expenses, logistics costs going down, etc. This will help you evaluate the efficiency of your data scraping and continue moving your work on business workflow improvement and optimization in the right direction.

Recently we did a big data scraping project for SEO analysis. Our client needed to collect all the links from more than 10 million websites that were restricted by special rules. Then he wanted to select URLs that matched set keywords in order to understand marketing trends in his business niche.

Having analyzed all the client’s preferences, we developed a custom solution based on a special web parser that is able to analyze URLs and output only those links that matched the client’s requirements. Also, we created a database to manage all the collected links. We used proxies for those websites that allowed access only for users from particular locations. We delivered the data to our client because we had already built our own solutions for large scraping tasks. Our client saved money by choosing data delivery and got the data needed for analysis without the cost of having to develop or maintain their own software.

If you need consulting regarding your big data scraping project, our expert Dmitrii is available to help. Schedule a free consultation.