Data management plays a major role in the success of an organisation. According to Wikipedia “Data management comprises all the disciplines related to managing data as a valuable resource”. As the name defines that it is the management of data. In these days data is playing a crucial role in business. Especially in ecommerce or retail sector companies uses data insight for each and every department for the better improvement in their services as well as improvement in company. They use data management to generate revenue, cost optimization and risk analysis.
We are now living in big data world. Data generated in vast amount with variety and complexity. Data are complex but it’s important too. In ecommerce industry data came from several sources. Managing these data is really a tough job. But to use those data for improvement of the organization we need to manage them in proper manner. So, a proper and effective data management is necessary.
Hadoop is a boat to travel in big data sea. Hadoop is the core and basic technology for most of the big data related solutions, planning and application. It is most highly ranked and used platform for big data analytics solutions and strategies. Hadoop has a great impact on each and every sector where big data is leveraging. Especially in Ecommerce, big data has its own importance. So, Hadoop has been frequently used in retail sector.
In recent years the shopping experience has changed dramatically. Now everything is available on internet as online shopping. The power has been shifted to consumer from retailer. Consumers having more options now than any other time. To compete in this environment retailers or ecommerce business changed their traditional plans and employ new strategies to attract and retain customers. Big data and Hadoop help them to connect with customers and in decision making.
Before going in deep of Hadoop we need to understand the concept of Hadoop. In simple words “Hadoop is a framework on which big data can be processed”. Traditional framework or relational database technology are unable to process big data because of its volume, variety, velocity and complexity. So, we use Hadoop framework for this purpose. In core of Hadoop there are two things, HDFS for storage and MapReduce for processing.
In retail sector MapReduce is used to integrate and analyse the large amount of data and the analysis result helps them in decision making. Some area where Hadoop can be used in ecommerce:
Personalised offer – Using MapReduce retailers try to know about their customers and their capabilities and according to their history they provide personalized offer to each customer.
Fraud detection – Using Hadoop retailers try to find fraudulent behaviour. They analyse the pattern of fraudulent and take decisions to prevent these things.
Social media analysis – Using Hadoop retailers analyse the sentiment of people about their products on different social media platform. It helps them a lot in improving their business.
Improving customer service – Hadoop also helps in improving customer service in ecommerce. Analysing the data of customers feedback companies improve their customer service to provide better shopping experience to their customers.
Predictive analysis – In ecommerce there is a very tough completion between all retailers. They always make plans for a short period of time with long period impact. To keep themselves in competition Companies use Hadoop to predict the future sales and after getting analysis result they make them ready for that.
Hadoop helps ecommerce business in many ways. Companies are using Hadoop for their data management and leveraging data to find better insight which they are applying in decision making. Now a days Hadoop became an integrated part of a successful ecommerce business. In other words ‘Hadoop is playing an important role in ecommerce data management’.
Recent shift in consumers’ acceptance of the benefits of online shopping experience puts greater stress on retailers and their strategies. Online shopping gives a stress free and uncrowded environment that ecommerce business offers from last few years. All activity in ecommerce business is done in internet world and hence, it generate a huge amount of data i.e. big data.
Big data is a huge collection of data comes from different sources such as social media, web browsing, and many other sources. Companies leverage these data to find useful information from it.
The most powerful impact of big data on business is identify hidden pattern and decision support. Decision making based on data insight always have a better probability of success than the decisions based on guess or gut feel.
Nidhi Agarwal, Founder and CIO, KAARYAH adds, “Big data makes a lot of relevance for ecommerce companies who want to stay agile and relevant to their customers. The companies are monitoring customer consumption patterns and convert them into product level inputs to improve products and introduce new products. Also, the speed of consumption combined with our agile production system leads to large working capital efficiencies.”
There are many areas where ecommerce companies leveraging big data and enjoying its benefits. Some of the area where ecommerce companies gaining benefits by leveraging big data are:
Data driven decisions – Almost all marketing or product related decisions is based on real time or near real time data analysis result. For making any decision, there should be a strong base behind that. Always decisions based on real time data information is more effective and fruitful.
Personalized offer – Analysis of data helps ecommerce companies to target their right audience in more effective ways. It helps customer to find what they want and as the result sales always become faster. Companies provide custom offers on the basis of customer interest and preference based on their earlier shopping history combined with their multiple other data sources. According to Amitabh Mishra, CTO, snapdeal “We have total 14 properties like ‘Viewers also viewed,’ ‘similar products,’ ‘trending now’, etc. on site for every viewers or customers who visited our website and the big data platforms when put together influence 40% of the orders we receive today”. It shows the strength of big data in ecommerce.
Supply chain management – Supply chain optimization is one of the most critical success factor of ecommerce business. Companies often leverage big data for their supply chain also. Using big data analytics, ecommerce companies plan their delivery route, reduced cost, preferred time for delivery and many more.
Fraud detection – Big data also helps in detecting fraudulent by analysing patterns, payment methods and browsing history. A report published by Aberdeen – a fact based research company says that after analysing different types of frauds and companies behaviour it came in picture that 16% of respondent say that detecting fraud was a primary use for their analytic suite.
Organized data – Organizing the data coming from multiple sources is also a big challenge in ecommerce business. All data need to collect, store and organize for the further use. Big data helps to find the way to organize data and enable business people to find useful insight from those data and apply in day-today decision making.
If you are in ecommerce industry – yes, leverage big data analysis for business decision. It really does not matter the size of your business today. Big data is one of the most important success factor of ecommerce business and by applying big data analytics, ecommerce you can make better business models to drive up sales day-by-day.
Today, retail is driven by data and technology. Big data is becoming really important to retailers. Retailers must adopt big data and digital skills to get succeed in a sector. According to a survey from “101Data”, 96% of retailers reported that big data was important to them and 48% of retailers reported that big data best fit with their marketing department.
As we know that the thumb rule of online retail marketing is: to know every product across your service area, to know every person to whom they interact and with having the best ability to connect them in a transaction.
We all have probably seen the big data and digital in retail sale. If not then you may experience this. For this you will need to go to a shopping site for online shopping. Add a watch in your cart and after sometime remove it from the cart. Now move out from that website. After onward every site you visit may found a watch ad of that shopping site. Online retailers use the data of your interest and customize the ad to you according to your interest. As a research of Amazon states that they had 30 percent of sales due to their recommendation engine. That is the use of big data and digital in the retails.
One of the famous example of taking advantages of big data by a company to drive success is Rolls-Royce. As we now that Rolls-Royce is famous to manufacture large engines which generate a large amount of power generally used in airplanes and ships. They generated a large amount of data during the manufacturing of a jet engine. They used these big data information in mainly three areas i.e. design, manufacture and after sales support. After using these data they found huge change in every field of their industry. They were getting more appropriate and best designs, Product went more error free and sales increased
These are the few application of big data and digital in retail sale:
Expected buying behaviour– To find the expected buyers of different products using the analysis of data. It’s like if a retailer has to sell a game then they will love to advertise for keen gamers and with the help of big data they may easily find those people.
Opinion about brands– To find the famous product among the buyers and organise their product on the basis of buyer’s choice. It also help us to get real-time opinions & responses.
Personalized Shopping– To provide discounts based on past shopping details like preferences and give discounts on the basis of their previous transactions. It help any organization to keep their customers for a long period of time.
E-Commerce optimization– In retail marketing customers are on driving seat. Companies are totally dependent on what customers want, it doesn’t matter what you sell if it’s not according to the customers need you will never get success. So, it’s essential to understand the user behaviour on the website to optimize sell. Big data analysis can provide the user behaviour.
Optimizing the store– Have you ever noticed that why essentials are at the far end and luxury goods at the start. This is the optimizing store. Using big data analysis they track user movements to understand their behaviour.
Price– Big data can also provide the base for prize optimization model.
Big data is the biggest reason for getting success in retail sale. It creates a lot of opportunities for marketing and sales. So, retailers are moving quickly into big data. Some companies are already using big data and digital and getting benefits of using big data and digital in retail sale and they got 5-6 percent increment in their productivity rates and profitability.
Today Big Data is changing the way of our thinking. It’s changing the way of living and working. We are leveraging Big Data in our growth so that everyone can contribute and take advantages. The big data analytics makes life easier and more goal centred. Analysing huge amount of data gives us more accurate decision making ability. With these benefits it is also affecting our social life. Our social life can revolutionize after applying the big data analytics. There are many area from which Big Data can revolutionize social welfare. I am listing out some of them with reasons that how it can revolutionize social welfare.
Online life will be safer– Now a days everything went online. Our life is almost dependent on online services. From Shopping to Education, Transaction and many more we used online services. But this method is no more so safe. Others may hack your information and misuse those data. Big data can help us in this problem. By analysing the hacker’s pattern it can improve the security of your website.
Education– Today’s education cost is rising twice than any other sector. So we need to find an alternative of these traditional education system. Big data can help us to provide these study materials online. So that all could have easily access to the education.
Health Care– The most impact of big data on our social life is in healthcare sector. It helps doctors to find the pattern of any disease and on the basis of that pattern medicines for that disease can be invented.
Transport- The advantage of big data is also in transportation. In transportation there are multiple of uses of big data from analysing the traffic to road safety and security purpose. Data scientists can find the behaviour of people on road. By analysing the transportation data the pattern of accidents can be identified and their solutions can be generated.
Career opportunities- There are many websites which help job seekers and employees to find their jobs or employees. Job-seekers find the opportunities according to their skills and employees used to get their best meet on the basis of candidates skills
Business future- To plan the future of our business we need to go for big data analysis. Those business decision will take your business far away from your competitors because those decision will be based on the real experience of your customers.
Weather forecasting- Big data can also help our social welfare in weather forecasting. It will give great benefits to all but specially to farmers because most of time they dependent on weather. So use of big data in this area will revolutionize the whole society welfare.
Big data creates a lot of opportunities for every sectors people just need to catch those opportunities for the development of their own and as well as society. One more great use of big data towards revolutionize social welfare is in anti-poverty programs. Big data helps to create difficult policies for the anti-poverty programs. For these type of applications a large database set is needed and linked to different social data sources to get huge amount of information regarding our social life. Then only we can apply these benefits to revolutionize social welfare.
Whether you agree or not, you probably need to look at ways to explore big data if you need to sustain in the industry.
Today, there are tens of billions of new Internet-connected devices and these devices send huge amounts of data to cloud or server. And, this data – big data, is a gold mine of information for business.
Recent research shows that the amount of data collected by enterprises continues to grow at a rate of 40 % to 60 % per year. And hence, it is prerogative for enterprises to start getting into big data and processing it – eventually, use it for decision support.
Big Data is a very large set of data and it could be unstructured, semi structured or structured data. It is huge, complex and very tough to handle using classical processing tools. We need special framework to analyse those huge amount of data. Also, we need multiple expert resources too for analysing these data. So, before starting the big data processing, it is good to check that whether we are ready for big data processing or not. Now, let’s look at some of the prerequisites – including people who needs in big data processing. Some check points before starting big data processing are:
Big Data lab – Big Data Lab is a dedicated development environment for experimentation within your current IT infrastructure with presence of big data technologies and approaches to process big data and analytics.
Though it is not mandatory, it is good to set up a big data lab to start big data processing. Is your big data lab setup ready to process the different steps of big data processing? If not then you are probably not really ready for processing big data. All the operations during processing will be occurred with the help of this lab. So, each and every component of this lab needs to be ready for big data processing.
Data Integration Capabilities– When we say data integration, it is essentially a process of connecting data to big data lab from different data sources. This phase or step is for connecting the data from its source to the technology – your big data lab. For this step, we need machine setup that can be used for this connection and the expert professionals who can perform data integration.
Data Development– In this phase, data is collected and arranged. Here, we may need a distributed database like HBASE for collecting big data and the developers to handle this operation with suitable tools and store the data in appropriate database according to the needs. Data developer needs to make the data readable for analysing tools like Hadoop, R, SAS, etc. All the necessary components for big data development and data processing to be ready in your big data lab with right skilled people should be available before data processing starts.
Data Analyst – To analyse big data, we need data analyst who can work on the data to find meaningful information. The role of data analyst is very important in the big data processing. Data analyst should be skilled i.e. know to apply different and appropriate analytic techniques for different types of problem. They should be experts of statistical and computational techniques. In short, we will need a team of analyst for this purpose. We must have a group of analyst having these skills before start processing.
Visualization experts– To visualize the analysis results, often we need visualization experts. The visualization experts must have ability to turn statistical and computational analysis into presentable graphs, charts and animation. They need to be expert in virtual art and design – more over business requirement. We may need these experts during presenting the analysis report to the client because client may not understand the big data table or any other technical things. So, we need to show them results in graphical or animated representation – it could be a simple dashboard. To be short, before processing big data you should have some good visualization experts.
Business Analyst– These are people who have knowledge of different area of business i.e. your business, industry, benchmark, pricing, marketing, risk analysis, finance, etc. They need to have ability to ask right business questions and a drive or orientation towards business objective of big data processing. If you have business analyst with these quality, then you can get a reason to analyse big data.
Data Scientist– In order to run the entire big data project and drive the big data processing exercise into meaningful and fruitful one, it is good to have a data scientist. Data scientist is an expert in the entire big data value chain with hands-on experience in big data tools and technology. Though it is a costly proposition to on-board a data scientist having end-to-end big data capabilities, you can consider hiring a consultant who can perform this job and get the project done.
To summarize, it is good to build big data processing capability as big data can help drive proactive business decisions. Once you have all the seven factors and resources ready, yes – go ahead and start exploring big data; and get prepared for a big leap in your business and growth.
Small business owner may think that big data is for large companies with big time technology budgets. In reality, it is not. Small business can also stand for big data benefits within available – small budget. A small business needs to look at big data in different perspective as they may not know how to start and where to start or may not know if big data exist in the company.
Big data and analytics has become indispensable for any business to stay ahead in this competitive world. If we look at any small or large enterprise having outstanding financial performance over a period of last 4 to 5 years, all will have one thing in common – all of them leverage big data for their decision support.
What does this mean to a small business? Yes, it is the time to start with a first step – if not started.
Today, most of the small companies know that big data is playing a vital role in success of small company. Knowingly or unknowingly many small size business are missing out the benefits of big data because of some wrong believes. Recent studies states that small business thinks that utilizing big data is too costly. Quite often, it’s not really so. You don’t always need to invest huge money in storage, hardware or not even on resources. You may choose cloud option for storage and outsource it to a right vendor for processing.
Let’s look at some ways a small business can leverage big data
Increased customer focus – Small companies can look at all possible data sources – internal and external data, which can help generating better customer insight. Focus on customer preferences can bring increased customer base, lead conversion and revenue. Big data can help finding these hidden insight which can eventually lead to increased lead conversion.
Generate innovative ideas & New product development – Recent trends shows that companies do not require decades to build a billion dollar business. It all can happen in few years leveraging big data. Big data and analytics play a vital role in this journey of quick growth and increased ROI. A small business need innovative ideas which can bring them out from the common line. With the help of big data and analytics, business can have different and accurate information to make innovative ideas and products. It can help small companies to gain competitive edge in marketplace.
As long as you are not investing on big data hardware and software, it is all about testing the water and check whether you can make better informed decisions and leverage them to stand out in the competitive market place.
Massive data-sets on everything from demographic to social data, weather to GPS data, consumer spending habits to government budgets – many are freely available online – if you know where to look and how to pull it. Also, there are many free big data tools available to make sense of this data.
Hiring right talent according to the need is the key factor for a company to be successful. “A good fit for the job equals a good fit for the company” is one of the most appropriate quote during hiring a resource.
Big data value chain is mainly divided in three steps. They are data integration, Big data development, and Big data analytics. We need different skilled resources for these three different phases. A person should be hired when his skills meets the needs of the requirement. Let’s look at these steps one by one..
- Data Integration– As we know that in Big Data, data comes from multiple sources. Connecting these data from different sources leveraging big data technology through big data lab, Amazon web services etc. for collecting data and ingesting to the operations is called Data integration. Data coming from different sources have to connect with the appropriate technology. We need ‘Big Data Admins’ for this purpose who will able to make connection between these two, they must know how to use different data integration tools i.e. Sqoop, Flume, etc.
- Big Data Development– Data comes from different sources in structured, semi structured and unstructured form. Those data need to be stored in an organized manner so different development tools can read it for processing. We need big data developers for this purpose who knows the different data processing technologies like Hadoop, Informatica, Teradata, etc. Their work is to make the data to be readable by data processing technologies. They should also know about different database in which data will be stored.
- Big Data Analytics– This stage contains data processing and converting the processed data for the decision support. Data analysts and Data scientists work in this phase for analyzing data to find out hidden pattern in the data and build statistical models. One of the favorite definitions for data analyst is “A data analyst is someone who is better at statistics than any software engineer and better at software engineering than any statistician.” – Josh Wills” This one line defines the characteristics and needs of a data analyst. A data analyst must be good at problem solving. Companies generally prefer engineering, statistics or computer science background people for this role.
To summarize, some of the key skills needed for Big data team are as follows:
- Hadoop– It is one of the famous big data working framework. Big data people must know this framework.
- NoSQL– On the operational side of Big data field distributed storage like HBASE are used. To work on these databases NoSQL should be known to the person.
- Statistical analysis– This is one of the important skills to be in a big data person. They should be familiar with different statistical modelling tools like R/Revolutio, SAS, SPSS, Alteryx, Mahout Libraries, Matlab and there are many more
- Data Visualization – Person should be familiar with different visualization tools like Tablueau, Spotfire, Qlikview, Rapid miner, MS Excel, etc.
- Programming language– Person to know the general purpose programming language like c, java, python, etc.
- Problem Solving– A big data person must be good in problem solving. So, they can find the solutions of different problems during the analysis.
Big data is comparatively a new field with a lot of opportunities. During hiring process, Companies need to pay attention to what they wanted and go for that. Though, it is not advisable to find people having expertise in all big data tools in three phases mentioned here and it is not necessary. But, it is important that people have a bend of mind for learning new tools.
Defining, articulating and representing business problems is a crucial initial step in any Big Data initiative. To deliver quick results from big data, it is good to have powerful and well organized analytic capability. And, if not? Nothing to worry. Reach out to a right big data partner who can deliver quick results – this could be a Proof Of Concept (POC). Once recognize the POC is successful and could generate business value – yes, go ahead to the next level.
Always, it is good to have internal analytic team who can work on data to solve problems and help finding innovative ways to serve customer better. Analytics team must have enough and good amount of data and an effective communication to deliver results. They also need appropriate tools according to the size and nature of data to perform operation. Analytic team has to look at big data life cycle – from data preparation to final report/model delivery or including model monitoring, if modelling is a part of final outcome. Each and every stage of the big data journey effects the final result and business impact and hence, it is important to have involvement of data expert – we may call him data scientist.
Companies are investing resources on technologies, operations, training and development of skills. But during the analysing of big data the most important factor is understanding of data and connecting with the business background and leverage them for decision support. If the analyst doesn’t have the understanding of these things then the corporate information doesn’t help them to find out the appropriate solution. For delivering quick results, analyst should pay enough time to understand the data and the business problem and more over the business itself. Analyst should have the understanding of customer issues and according to the issues they have to decide, what can be done about it and what tools to leverage.
Let us review factors that drive success when companies try to deliver quick results with big data. To deliver quick results with big data, we will need to consider some of the these points..
Business Understanding – “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute resolving it,” Albert Einstein said. Before making an attempt to solve any problem, we should step back and invest time and effort to improve the understanding of the question that what we are trying to solve. For delivering quick results, we need to have good understanding of the business.
Strong Strategy – Drive to Solution: To run any successful operation a strategy is essential. There should be a strong strategy having a vision that what needs to be done before starting to analyse the data. The whole big data approach should have a strong and clear vision and drive to final output.
Data understanding – Data is the main and the most important factor during the analysis. Whole analysis is around the data. Analyst should have knowledge to use and structure data according to their needs. Data should be organised and well structured, so that it will be easy to work on that. Data should be clustered according to their logical type. So an analyst will target the focus area for the data operations. The area will be identified according to their impact on business.
Smart analytics team: Not size of the team – but, quality of the team. While recruiting the analyst or statisticians, recruiter needs to look for high problem solving capabilities, and reasoning power of candidates. Candidates should have the skills that – how and why to approach a problem. Engineering background candidates may be a good analyst.
Expertise on Big data tools and technologies– To deliver quick results, big data team should have expertise in big data tools and technology. It will help the big data team to get in the core of data.
In a recent survey of “The Economist Intelligence Unit” which has been done after completion of a big data project, one-half of analyst said that they didn’t had enough structured data to support decision-making, compared with only 28% who said the same about unstructured data. In fact, 40% of respondents complain that they had too much unstructured data
Ability to leverage right tool – Once you have the skilled analyst and right data with a strong strategy, you need to look for the correct technology on which the data would be analysed. Technology acts as a bridge between the skilled analyst and the right data. So right technology is needed to operate those data by the analyst. There are different technology which can be leveraged for this purpose according to the requirement.
Governance – It is very necessary to connect all the resources and technologies as a single unit to deliver quick results. Governing the whole process in well-mannered way plays an important role in delivering quick results. Governance body need to evaluate the team and assign the works according to their potential and skills.
To deliver quick results, start with a POC and ensure that result is out and useful. In order to start the POC, it is necessary to have deep business understanding, right mix of skilled people, ability to choose right tools and techniques with a powerful strategy that makes the result faster and accurate.
Companies are vying different ways of discovering the value of letting customers create their own unique products. Almost all e-commerce giants leverage Big Data to present a personalized set of products to their customers and Amazon is a successful example.
Now, let us look at how small and medium size retailers can explore the driving force – big data, and how Hadoop can help in this journey.
Hadoop is an open source tool for processing big data. It is an open source framework where data can be stored and processed. It is one of the most used and highly ranked platform for big data processing. Hadoop brings many advantages while applying it in processing of big data. Hadoop allows users to handle increasing volumes of data quickly and efficiently. That makes it friendlier with retail sector –ecommerce as well. There are many practical advantages of using Hadoop.
Hadoop having two parts in its core, one is HDFS (Hadoop Distributed File System) for data storing purpose and other is MapReduce for processing data. Whenever any data comes to Hadoop it breaks those data into small “chunks” and then those small-small part of data store in different Hadoop clusters across the server.
Hadoop framework is extensively used for ecommerce data processing that comes from different sources and analysis. Processing data using Hadoop is a cost effective way to find insight.
Shopping experience has been changing from traditional offline way to online marketing. Concept of brand is getting replaced with customer personalization. Now power has been shifted to consumers from shoppers. So, all ecommerce companies try to attract consumers with many plans. There are many application of Hadoop in ecommerce because of its cost effective data processing characteristic. Some application of Hadoop in ecommerce sector are:
Personalized offer – As we discussed above that shopping experience has been changed in recent years and power shifted to consumers from shoppers. So now customers are important. All ecommerce companies want to treat each customer in personal manner. Customers shop with same retailers in different ways. So using Hadoop retailers collect data of same customer from different sources and provide personalized offer for them.
Improve customer service – Online retailers use big data for a good customer service. Using Hadoop they track the customer data whenever customer contact representatives then customer data should be in front of customer care representative, so they won’t need to ask anything from customer and customer will feel special.
Fraud detection – Using Hadoop retailers detect the patterns of fraudulent. Hadoop is the simplest and best method to detect the pattern of fraudulent. Any other method will be cause for high expenses without certainty of correct result.
Dynamic pricing – These days in ecommerce sector competition is too high. So always each organisation needs to be alert about other rival companies that what they are doing and how? For example pricing. As a customer you may find some difference in price of same product on different retailers. So, companies are using Hadoop to find the changing pattern in price of their competitors and be ready for those situations.
These are the few ways through which we can know that using Hadoop in ecommerce business is a cost effective way to get a solution rather than any other way. The use of big data in business make the business more attractive and successful, and Hadoop makes it even more appealing. So ecommerce companies are steadily moving to apply Hadoop to increase returns and reduce effort.
Web Scraping is a technique to extract large amount of data from websites using some programs or applications and save it to your computer or to a database for further use. It is a technique to automate the process of collecting data from any website instead of collecting data manually.
Whenever any website that doesn’t have their API to pull data for the user then web scraping techniques can play an important role. The beauty of web scraping is that you can scrap almost any content that viewed on a web page.
These days’ web scraping solutions are in the range from traditional way of manual effort, semi- automated to fully automated scraping.
Automated web scraping is often done using custom scripting or automation tools. Python is a powerful scripting language for web scraping. Codes written in Python can be connected to website from where we want to pull data. Some big websites like Google, Twitter, Amazon, etc. having different APIs which allows third party tools to pull data from their website with some terms & conditions. So, mining these websites are not a tough call under some finite range of data provided you have an expert support. After completing that range, they charge for extra data. Scraping these websites using hard coding without their API will not be a wise decision. It may be a cause of legal issues or even blocking your IP.
In this article we will mainly focus on second type of websites that haven’t any API to pull data from their websites. To pull data from these types of website we use hard coding or web scraping software. Here we will see about that hard coding and how python is powerful for this purpose.
Python is a scripting language which can be used for various purpose, especially in big data python is used very frequently due to its user friendly characteristics. Python is the most used language for scripting web scrapping. There are many packages available in python which supports web scrapping. Some of them are:
Amazon API Wrapper
This module offers a light-weight access to the latest version of the Amazon Product Advertising API without getting in your way. An object oriented interface to Amazon products which supports both item search and item lookup. Using this package you may pull Amazon product data from Amazon website.
A module to scrape and extract links, titles and descriptions from Google search results.
This module help you in book search on Flipkart.
Scrapy is a fast high-level web crawling and web scraping framework, used to crawl websites and extract structured data from their pages. It can be used for a wide range of purposes, from data mining to monitoring and automated testing.
A powerful python module to find files in the set of paths.
A python module for scrapping data from any page. You may collect all data or some specific data using this pythn module.
This module provides multithreaded crawling, reporting, and mirroring for Web and FTP in one convenient library. Crawling depth, maximum number of URLs to crawl, and maximum number of threads are user-configurable. You may adjust all these attributes according to your requirement.
Today, web scraping is a powerful and economical way for web data mining or as the source of big data. Many specialized companies are focussed only in providing web scraping to clients.