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Challenges in Outsourcing Big data

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.


Sandboxes and their advantages

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.



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:

  • Development
  • Quality Assurance
  • UAT ( User acceptance testing )
  • Production

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.

7 check points to find whether you are ready for Big Data processing internally

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.

Hadoop: Cost effective way to process Ecommerce data Processing

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.

Hadoop for Ecommerce data processing

Retailers always want real time or near real time analysis of huge data sets that change rapidly or have a very short life, for example web shopping cart. We know that Ecommerce companies sit on huge amount of data due to a large number of transaction & inventory. And for that, retailers leverage Hadoop technology for quick and large volume data processing.

Data processing is a process of manipulating the stored data for further use. Stored dump data need to be converted into meaningful and can be used for decision support. So, after processing the data, it can be fit for different purpose as per requirement. After processing, data format may change, means data may be modified and it cannot be the same that it was earlier.

Hadoop is one of the highly used platform for big data processing. Hadoop has established itself as the highly demanded tools in big data sector. Hadoop is used for data storing as well as data processing. For both purpose it is having different part inside it- for data storing HDFS is there and for data processing MapReduce is there. With the help of Hadoop, retailers started shifting their focus on individual marketing by giving customized retail experience.

Hadoop is the widely used framework for big data processing and MapReduce is the most important massive data processing tool for ecommerce data processing. Once Gartner had predicted “Hadoop will be in most advanced analytics products by 2015” and now we can see that their prediction became close to 100 % correct. There are many reports published on Hadoop which convey about the importance of Hadoop in Big data. Some of them are:

A report of Technology Research Organization says that “The data market currently with the fastest growth are Hadoop and NoSQL software and services”.

According to the Big Data Executive survey “Almost 90% organisations which are leveraging big data have embarked on Hadoop related projects and thus Hadoop skills are in huge demand”.

These are some survey reports that convey the importance of Hadoop in ecommerce data processing.

Now we will see that how and why we use Hadoop for data processing. First see the answer of How?

Hadoop is an open source data management technology which having both data storing capacity as well as data processing. Hadoop distributed file system i.e. HDFS is used for data storage and MapReduce is used for data processing. Whenever data come in Hadoop it break all data in small chunks and store it on different clusters across the server. After storing data, MapReduce job runs according to the requirement.

Now we will answer the question of why i.e. Why ecommerce uses Hadoop for data processing?

Using Hadoop, ecommerce companies process data to utilize big data insight to ensure high profitability. Some of the area where they use analysis result that comes after data processing are:

  • Personalized marketing
  • Fraud detection
  • Improved customer service
  • Dynamic pricing

These are the few areas where Hadoop helps ecommerce sector to ensure high value service.

Hadoop having some advantages that make it better from other tools. It is based on distributing computing concept that makes it different from others. Due to its scalability and effectiveness, companies are heavily adopting Hadoop for data processing.

Small Companies & Big data processing: Way forward

The importance of Big Data is increasing in every passing day. Each and every organization wanted to implement the insight of big data to their business. It is not only applicable to large size of organizations but also for small and medium size companies are also expected to leverage big data for their business to propel growth.

A response from a recent Nielsen poll of 2,000 small businesses in the U.S.A, 41 percent think conducting market research is too costly, and 42 percent say they just don’t have the time. And even more surprisingly, 35 percent went so far as to say they’ve never even considered it.

Research shows that, the companies that have the best data and ability convert them for decision making is likely to win the battle. In other words, big data will play a key role in deciding the winner.

There are many rumours about the usage of Big Data to small and medium size companies i.e. Big Data is not applicable for small and medium size companies, cost of leveraging big data is too high and many more. The fact is that Big data is equally important for big as well as small companies. The cost of leveraging big data is not too high.

Let us discuss some area in small companies where big data insights can be implemented:

Information about customer insights: – By leveraging big data, you may know more hidden insights about the customers’ requirements. As big data brings a lot of information about the customer choice, you may provide your products to the customer according to their current and potential needs. Especially retail sector is using this concept in their business than other sectors and results are visible in our daily shopping experience. And today’s business, it is totally dependent on customer satisfaction – all about customer experience management and ensure collect data at each customer touch point and convert them to informed decision making. This can make customer feel special and delighted.

Better product / service offerings: – Either you have a small company or big company; you must know about your product and customer’s requirement and match of both. If you are offering a high quality product but it’s not according to the customers need; then it will give you zero value. So, to know about the customers need is the first step towards offering any product and for this purpose use of big data can tell you a great story.

Continuous improvement: – Once your product came to market then you need information about that product from customers i.e. feedback. You could only work on the same product again if feedback are positive otherwise you need to revise your plan and product. Big data help you in this purpose to find information as feedback from different sources.

Value chain optimization: – Customers requirement, their preferences, demand forecasting, source of raw materials, processing, supply chain management, vendor management, sales and marketing, human resource support, etc. all these make an eco-system for a company. To be precise, companies having capability to capture data for the entire value chain and leverage them for decision making can improve the way it runs. Big data can better help you to collect these data points and can transform the way to minimize cost and maximize the efficiency of the value chain.

Marketing: – Almost every big company’s decisions are data driven. Strategic plans or especially marketing plan is drafted on the basis of information obtained after leveraging big data and advanced analytics. Data driven decision are more powerful and economic rather than that decision which has taken without it.  This can be also applied in small smart companies and definitely help keeping your business in front line.

In summary, companies – small, medium or big do not think a lot about their cost and own capability of big data; just get into it. In upcoming years, big data to be an important tool for survival and growth of any company.