As you know that big data is a collection of huge unstructured and semi structured data. There are different steps for processing big data from data integration to present the results to clients. Big Data processing needs several resources in which you will have to invest money. It needs several hardware, software and human to analyse. We will see the cost for each and every resource in detail below,
Data are now key factors for every sectors and functions in any size of company. Every sector and department couldn’t strike without dealing with data. So, Bigdata is playing an important role in the improvement of a company. The use of Big Data is to retrieve important and useful information from the large amount of unstructured or semi structured data.
Bigdata is one of the most trending word in today’s market. The effect of big data in every business- from fortune 500 enterprises to start-up’s is so huge that each and every company wants to leverage it. It doesn’t matter, in which field you are working and what is the size of your company? Data collection, analysis and implementation impact your business in several ways. This is the time where you can’t ignore big data analytics and if you are still saying that ‘Big data is not beneficial for my company’ then you are definitely moving out from the competition.
Outsourcing refers to the contracting with another company for business purpose. It includes both international and domestic contracts. Sometimes outsourcing also refers to exchange or transfer of employee and assets between different firms. It helps the firms in reduction of cost and improvement in quality.
Can I outsource big data to vendors? Before going to the answer of this question let us look at the different terms used in this question i.e. ‘Outsource’, ‘Big Data’ and ‘Vendors’.
What is outsourcing? Outsourcing refers to the contracting with another company for business purpose. It can be international and domestic contracts. Sometimes outsourcing also refers to exchange or transfer of employee and assets between different firms. It helps the firms in reduction of cost and improvement in quality.
Today, data is a powerhouse for generating business and exploring growth. And, the beauty is – it doesn’t do anything unless someone know how to explore it. It is never been so easier to solve business problem and uncover new opportunity in ‘big data’ field. As we know, Big Data refers to the data comes from millions of sources i.e. from social media, emails, surfing, cell phone signals, sales transactions, etc. All these data that can be stored can call – big data. To use these data i.e. big data, for the business purpose, we need in-house big data team or we need a big data partner who can help to collect, store, process, analyse, and provide greater insight for decision support. (more…)
If we talk about the development of Hadoop technology then there are two companies which are doing a lot in this field. One is Hortonworks and another is Cloudera. These companies are developing a lot of new ideas and software in the field of Hadoop to make it easier to use and developing a lots of applications on them. These companies provide tools to use and learn Hadoop.
Master data management also known as MDM is a process of creating and managing all critical data to one file as a single master copy i.e. master data. In a larger organisation there are many different departments. In each departments there are many number of software systems and each system having large amount of data to share or to use. Overall a huge amount of data are flowing here and there in the whole organisation. All these data need to connect in one file, called a master file that would provide a common point of reference. So, we can say that “Master data is basically a shared master copy of data from different departments such as product, suppliers, employee and customer used by several applications within an organisation”.
When we heard the word “Sandbox” then suddenly our mind clicked about it as a low, wide container filled with sand in which children use to play. But things are different here. We are going to talk about the sandbox used in developing in the field of software development. Basics are quite same but things are different. By providing sandboxes to a child we create an environment of a real playground with some resources and restrictions. Similarly in a software sandbox we create an environment in which development can be done with some tools as resources and some restrictions over what it can do.
So we can write a sandbox as a technical environment in which software development can be done and whose scope is well defined.
Software project having mainly four areas through which every software development steps processed. These are:
- Quality Assurance
- UAT ( User acceptance testing )
All these phase needs sandboxes to deliver their results fast with less risk of technical errors. We have categorized these sandboxes in five different types according to their uses in development process. Those are:
Development – These type of sandboxes provide an environment to developers and programmer to work or develop software with their separate set of tools that comes with the sandbox as a package without affecting the rest of their project team. Hortonworks Sandbox is an example of this in which all related and required tools come along with Hadoop working environment.
Project integration- These sandboxes are used to integrate the environment between a team. As we saw in development process that every team member having their own sandbox, so project integration sandbox establish an environment in which all the team members can exchange data and information to each other and validate the work before sending it to Quality Assurance sandbox.
Quality assurance – These sandboxes are useful in the testing process where it is shared by several teams and is often controlled by a specific separate team. The purpose of this sandbox is to provide an environment as real as the real time use so we can test our applications in different conditions. This sandbox is very useful when many applications access the database but it is same important when a single application access the database. We need to test within this sandbox before approaching to the User Acceptance Test.
UAT sandbox – These sandboxes are used for the acceptance testing purposes. This is the pre-step of production. So these sandboxes provides a real time scenario where the user acceptance testing can be performed.
Production – This is the final stage of software creation i.e. software has to release in this stage. So these sandboxes provide an actual environment in which the software has to establish.
The primary advantage of using sandboxes are that it always contains a package of software for the respective software development, so it makes the developers work easy and reduce the risk of technical errors.
OSP uses all these types of sandboxes while working on any project. With the help of these sandboxes we provide fast and better services to our clients. We provide an error free solutions by using these techniques. By using these sandboxes it’s very easy for us to provide a setup to our clients in less period of time. These type of special techniques make us different and unique for our clients.
A few years back, it was all manual data mining and it took long long days for almost all small and medium players in the market for web data mining. Today, technology is evolving a lot and we are in an era of Big data and manual data mining is no more a right method and it is mostly about automation tools, custom scripts, or Hadoop framework.
Now, let us discuss something about web data extraction. It is a process of collecting data from World Wide Web using some web scrapper, crawler, manual mining, etc. A web scrapper or crawler is a cutting tool for harvesting information available on internet. In other word web data extraction is a process of crawling websites and extract data from that page using a tool or programming. Web extraction is related to web indexing which refers to various methods of indexing the contents of web page using a bot or web crawler. A web crawler is an automated program, script or tool using that we can ‘crawl’ webpages to collect multiple information from websites.
In the world of big data, data comes from multiple sources and in huge amount. In which one source is web itself. Web data extraction is one of the medium of collecting data from this source i.e. web. Companies which are leveraging big data technology are using crawlers or programming to collect data. These data comes in bulk i.e. billions of records, or as a data dump. So, it needs to treat as big data and bring into Hadoop Eco system to get quick insight from it.
There are multiples areas where companies can explore web data extraction. Some areas are:
- In ecommerce, companies use web data extraction to monitor their competitor price and improve their product attributes. They also fetch data from different web sources to collect customer review and using Hadoop framework they do analysis – including sentiment analysis.
- Media companies use web scraping to collect recent and popular topics of interest from different social media and popular websites.
- Business directories use web scraping to collect information about the business profile, address, phone, location, zip code, etc.
- In healthcare sector, health physician scrap data from multiple websites to collect information on diseases, medicine, components, etc.
When companies decide to go for web data extraction today, then they move ahead thinking about big data because they know that data will come in bulk i.e. in millions of records will be there and it will be mostly in semi or unstructured format. So, we will need to treat it as big data and use Hadoop framework and tools for converting it for any decision making.
In this whole process, first step is web data extraction, that can be done using different scraping tools available in market (there are free and paid tools are available) or create custom script using programming language with the help of expert in scripting language like Python, ruby, etc.
Second step is to find insight from the data. For this, first we need to process the data using the right tool based on the size of the data and availability of the expert resources. Hadoop framework is the most popular and highly used tool for big data processing. Also, for sentimental analysis of those data, if needed, we need MapReduce which is one of the components of big data (Hadoop).
To summarize, for web data extraction, we can choose different tools for automation or develop scripts using programming language. Developing a script is often minimize effort as it is reusable with minimal modification. Moreover, as the volume of web data is huge-what we extract, it is always advisable to go for Hadoop framework for quick processing.