3 Major Differences Between Web Scraping and Web Crawling

3 Major Differences Between
Web Scrapping and Web Crawling

Shahabudin K
Content Creator

5 min read | October 8, 2021


           There are 3 Major differences between web scraping & web crawling, which many organizations misunderstand almost all the time. Web Scraping and Web crawling are two terms that are often used interchangeably in the Big data industry. This is a common perception that most of the users or enablers have in general. However, there are some major differences between web crawling and web scrapping. Further, these differences  will give you a clear idea about  both the terms. Let us walk through the article to understand the 3 major differences between web scraping and web crawling. 

Understanding the Terminologies:

Web Crawling:

                  Web Crawling is a Process by which, a specialized bot often called a “Spider”, crawls over numerous websites and URLs. Further, it spots the relevant content and then gathers the crawled URLs and Websites. Meaning, the crawled content is now kept in a particular place for the user to access it easily. This process is commonly known as Indexing. Often internet sites like Google, Yahoo, Bing are search engines that make utmost use of this service to bring relevant content to the User’s Intent. 

Web Scrapping:

           Web Scraping is a process in which you can gather data from any source. Be it a website, or an excel sheet with a trillion-attribute data. Thus, from a process perspective, Web Scraping is more of a contextualized process when compared to Web crawling. This is because using Web Scrapping you can extract data under a particular topic.  Like extracting the costs of a certain product or reviews of certain products and similar examples as well. 

Requirements for the Process(Web Scraping and Web Crawling):

In the case of Web Crawling,
  • To crawl across multiple websites within a short span, you must build a system with a Versatile architecture. This system must be able to crawl amidst changes in the websites.
  • One should keep in mind not to harm the target websites. So, it is mandatory to design a crawler that acts in a polite manner.

  • Being Language neutral is a considerable expectation from the audience side, which can help businesses target websites all around the globe in multiple languages. 

  • A good crawler over multiple sites and help  compress  the collected links and data, to enhance storage demands making the system more efficient in the overall process. 
In the case of Web Scrapping,
  • Since, scrapping scenarios are illegal in certain use cases, it is highly important to design a framework that is clearly ethical as possible.

  • Being ready with Proper APIs of websites, which can help recover data from websites. 
  • To carry out a perfect scrapping scenario, make sure that you have the apt third-party HTML libraries. These Libraries helps in sending HTTP requests to the URLs of the target website.

  • Parsing of data from the extracted HTML format, making it easier to organize data in the demanded format. 

Business Use Cases for Web Scraping and Web Crawling:

Web Crawling Use cases include:

  1. Monitoring News content across various media platforms.
  2. Lead Generation (Getting contact details of Potential Clients) 
  3. Competitor Research (Example: Price Monitoring) 
  4. MonitoringYour Distributors

Web Scrapping Use cases include:

  1. Predictive analysis, wherein the process harvests data across multiple pages of websites, to get a clear-cut prediction. 
  2. Insurance Companies make use of web scraping, to analyze risks before introducing a new policy to provide the best outcome of their offerings. 
  3. Marketing Research teams of various companies make use of Web Scraping to get data from multiple websites by defining the requirements to understand the market space much better. 
  4. Recruitment Companies collects job postings from various websites to provide the required Job opportunities to Job Seekers with the help of Web Scraping. 


               Therefore, Web Crawling and Web scraping are two sides of the same coin, where at times you find the concepts & the outputs to be much similar. However, from this blog, we hope you have understood some of the major differences between Web Crawling and Web Scrapping. If you are a business looking forward to Web Crawling or Web Scraping, help us understand your requirements by fixing a time when you are free for a discussion. We will help you by delivering results that will be beyond your expectations, as our global clients say. 

For more details visit us at : BeezLabs

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Digital Transformation for the Chemical Industry

Digital Transformation for the Chemical Industry

Shahabudin K
Content Creator

5 min read | Apr 27, 2021

The need for digital transformation:


               Digital Transformation in the chemical industry can help reap growth and innovation simultaneously.  Hence, it is important for chemical industries to understand this modernized solution. Consequently, this helps business make effective impact on its return on investment.

 Further, digital transformation could help chemical industries make effective usage of human resources by enabling Automation

               Firstly, the growth oriented chemical industry must be ready to adapt new changes. Moreover, adapting and adopting changes can help a chemical business to be at high par with digital transformation.

Digital transformation for Chemical Industries:


                  Chemical industries serves a large range of business-to-business and business-to-consumer communities. Therefore, handling business processes becomes a challenge.

For Instance, specialty chemicals satisfy both b2b & b2c sectors. Ultimately, specialty chemical industries are required to record the business process that happens.  Subsequently, these records can help run business effectively.

So, here is where digital transformation comes into action. Consequently, chemical industries go for an ERP system to manage their day-to-day business activities. 

As a result, around 60-70% of the manufacturing industries use SAP. But how can it be implemented effectively? How to differentiate your business growth from the others? 

Overcoming a challenge that the industry commonly face can help you rank above in this business.

        In brief, the main challenge of Adopting SAP is the manual tasks that happens in SAP. This requires huge manpower & high infrastructure costs.


With the advent of Automation and AI, industries started adopting RPA automation to reduce manual effort.



However, Conventional Automation finds it difficult to adapt changes offered by SAP. Therefore, Industries must adopt an Automation which is versatile enough. 


“Modernized ERP Automation is the Key”

Versatile digital transformation for Chemical Industries:

    “Reduce complexity through Standardization”

Most chemical industries fall into the traps of Automation that happens outside SAP. Ultimately, leading to the raise in infrastructure costs & number of SAP licenses. 

 However, to avoid complications chemical industries must adapt to modernized ERP automation . Selecting a Versatile ERP Automation services can  give a perfect fit for business.

           We Beez Innovation Labs are helping chemical industries adopt to Modernized ERP Automation on a global level. 

In conclusion, adapting to changes and using a versatile tool that business demands is the key to success.


Check out our blog to know more about “Modernized ERP Automation


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Understanding Deep Tech Startups

Understanding Deep Tech Startups

Shahabudin K
Content Creator

5 min read | Apr 22, 2021

Quick Introduction:

                            Deep tech startups are the companies which use a unique set of protocols and revolutionary technology which sets them apart from the rest of the startups. The term “Deep tech” was first coined by Swati Chatruvedi, Founder/CEO of Propel(x).

Footprints of Deep tech:

                                    Deep tech involves fields with respect   to Artificial Intelligence, Machine Learning, Robotics, Automation, Blockchain etc., As all these terms suggests, it is evident that with these varied technologies the sectors requiring immediate revival can be made more efficient, if deep tech is in action.

The Antonym of Deep Tech:

                              Shallow Tech which acts as a strong opposite of Deep tech involves reusability of the existing technologies. Meaning, it involves the same old  model of activation and productivity. On the contrary, Some famous companies like Uber follow a business model which runs on shallow tech. Above all, From a business point of view any strategy can be followed for success in the market if it works out well. But giving a deep tech touch makes a business remarkable and will serve as a reference model for upcoming businesses.

How can Deep tech startups help revive the Indian Economy?

                                Intelligent Automation supporting huge business enterprises help in scaling up productivity. Eventually, this increased domestic production will scale up the GDP as well. Above all, growing startups can adapt to deep tech involving AI, Automation, which will lead to the effective usage of resources. However, adoption of Deep tech might seem tough in the short run, whereas it proves to be more productive in the long run.

What does it take for an organization to adopt deep tech?

                                   For an organization to adopt deep-tech, Firstly, must be ready to learn and implement change management. Further, With change management comes the mindset of adapting to new tech in the market, then comes digital transformation. Also, Startups helping other start-ups will throw light on how technology is leveraging business. Further, this will turn the eyes of giant corporates towards these startups, turning everything into a profitable business opportunity.

Now, What is your take on Deep tech? Are you a Startup owner? If “yes”, what deep tech factor have you used in your organization? Let us know in the comments.

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Balancing your Balance sheets with Artificial Intelligence and Machine Learning

Balancing your Balance sheets with AI and ML

Content Creator

5 min read | Apr 15, 2021

Balance sheets:

A company’s current fiscal strength, valuation, IPO releases, the attraction of Investors, and almost everything depends on its Balance Sheet. How? Years-long, balance sheets have been a key for understanding the income, expenses, SKU numbers, interest rates, asset values of a company.

As time passed, the modern world started requiring Modernized solutions like AI and ML, for a quick prediction of the future expenses, a short prediction of how a company can cut down interest rates, the level to which assets can be monetized in the long run, etc., These are a very few modern expectations arising from the business world when it comes to balance sheets.

With the evolution of Automation and AI, these predictions proved to be effective to a maximum level. However no system is 100% efficient, that is why a prediction is happening instead of accurate outputs. Now let us see some of the important aspects in a balance sheet where ML and AI can help.

An increase in the income structure, optimization of Operation costs, bringing down expenses are some of the must achieve targets in any balance sheet. One target in which businesses fail is ” Repayment of Debts” with “Interest”. Can AI help you pay the interest and debt amounts? No. But AI can help you in predicting the range of interest rates that your company will reach within a particular period, which can prove helpful for your finance manager to understand at what rates debts can be settled to free the balance sheet from interest rates and making the debt rates come down to zero.

Interest Rates over debts:

“When a gift is deserved, it is not a gift but a payment”

An increase in the income structure, optimization of Operation costs, bringing down expenses are some of the must achieve targets in any balance sheet. One target in which businesses fail is ” Repayment of Debts” with “Interest”. Can AI help you pay the interest and debt amounts? No. But AI can help you in predicting the range of interest rates that your company will reach within a particular period, which can prove helpful for your finance manager to understand at what rates debts can be settled to free the balance sheet from interest rates and making the debt rates come down to zero.

Revenue Forecasting in balance sheets:

                Revenue in business terms is the expenditure added to income in the balance sheet. You might find this definition a common one, but the results which we are going to get out of this simple formula are vast. Meaning, Revenue of a company decide almost every aspect of the budget in the upcoming financial year.     

              What if AI can do this prediction for you, for example, say for the next 5 financial years, Insane right? It can do even more. In basic aspects of financial management, we can predict the estimated revenue and other terms by using Financial ratios.

               Ratios can give you a certain level of output, but still, it is manual & time-consuming too. If you want to stay ahead of time and also make your company adapt to modern trends then you can bring AI into the picture, training an AI model with numerous balance sheets of all models and sizes with different data can help predict for the upcoming years, this will help your CFO and stakeholders where to concentrate more so that they can increase their revenue.

Failure of CAPEX:

“Money is always eager and ready to work for anyone ready to employ it”


                 The smartness of a business persona is making money work for you. Meaning, the expenditure which you had made on your capital assets, must yield a good percentage of ROI. The source of money for spending on your CAPEX depends on various sources, what if all these go wrong? You get hit by a strong debt, which will eventually lead to the failure of the assets, increasing the depreciation value of the assets which in turn will lead to bankruptcy at a certain point.

                 So how can AI/ML can help in this scenario? Again prediction modeling comes into the picture, your AI model can help analyze how much the depreciation cost might hit after a few years or the next year too, it can also help you understand whether investing in a particular asset is worth it or not. Which can help you preplan your monetary allocation on CAPEX saving your company from numerous pain points.


                    AI & ML are aspects that can help you give a predictive analysis to make your company prepared to face challenges. Any system isn’t 100% efficient, AI is no different from this. But the results will prove sufficient enough to take care of your challenges and over a while, this can skyrocket to any percent level for that matter, meaning the sufficient results given by AI can increase the strength of your balance sheet year on year.

Hope this helps. Now, tell us in which financial aspect you think AI can help you resolve your challenges.

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