What is Big Data and difference between online and offline Big Data?

This report is based on topic of Big Data and its applications. Today in different industries Big Data is used such as Supply Chain/Logistics, Healthcare, Insurance, Finance and Marketing etc. The main reason of high demand of Big Data applications or solutions is that it is considered better than traditional methods for handling huge amount of data. Big Data is a term that describes both structured and unstructured large volume of data that is used in business at regular basis. Here amount of data does not matter, but what’s an organization do with data, it does matter.

For knowing about business insights, better decision making and for strategic business moves Big Data can be analyzed. Now the purpose of this report is to select an application of Big Data in a particular industry and to perform comparison of major values added by Big Data Technology over conventional methods. The main points that we will consider here in this report are what big data is, comparison between online and offline big data, how to select right application of Big Data for business and other essential concepts of Big Data. We have selected an application of Big Data in Healthcare industry and that application is Hadoop. Now we will discuss important points regarding use of that Hadoop solution of Big Data in Healthcare (Analytics, Morabito and Publishing, 2017).

Before start discussion about Hadoop as application of Big Data, the basic knowledge about Big Data is necessary.

What is Big Data and difference between online and offline Big Data?

Big data is a new act used to collect and store huge amount of data. The definition of Big Data is defined as three Vs such as Volume, Velocity and Variety (TechRepublic, 2017).

Volume: There are various sources from where data is collected by organizations such as social media, business transactions and sensors’ information. In earlier time, the management of Big Data would have been a problem, but with the use of new technology such as Hadoop have reduced the burden of managing large amount of data (Gewirtz, 2017).

Velocity: The velocity in big data refers to the speed of data processing. This V of Big Data measures that how fast the data is coming in. E.g.: In case of Facebook, several images has to handle every day. In this case, by using Big Data, it becomes easier to process, file and retrieve that much amount of data from database (Insights, Insights and Data?, 2017).

Variety: Data comes in diverse formats such as structured data, numerical data from traditional databases and unstructured data from text documents, emails, videos, audios and transactions related to finance. Big Data has potential to manage different types of data.

Big Data is categorized as Online and Offline Big Data. Following is the difference between Online and Offline Big Data.

Online Big Data

This type of big data system provides competences of operations for real time and interactive workloads where data is consumed and stored. The main applications of online Big Data are social networking news feed, analytical tools, real time ad servers and CRM applications.

Selection of right big data application for Business and Project

Offline Big Data

This type of Big Data system offers analytical capabilities for surveying and sophisticated analysis that may relate to most of the data. The example of offline big data technology is Hadoop.

It is not easy to figure out that which type of Big Data will be good for us. Most likely, we need both type of Big Data systems.

There are various applications exist of Big Data and among all these applications, the selection of right Big Data application is necessary for our business, project and desired outcomes. Here we will discuss that how right big data application can be selected for business purpose.

Selection of right big data application for Business and Project

Big Data has become the newest and popular way to get perception into our customers and finding new ways to complete in the marketplace. But it is only possible if an appropriate big data application for business and project will be used. Here are some key considerations that must be implemented to select a right big data application (ComputerWeekly, 2017).

Do our organization already have a big data platform?

If the answer of this question is yes, then it does not make sense to implement one more big data platform. In this case, we need to consider a combined, consistent and cross functional architecture. This will provide offers for cross business analysis (Bodhtree Blog, 2017).

Identify the data platform drivers

There are two types of data platform drivers such as storage and advanced analytics. Before selecting a big data application for business organization, it is necessary to identify data platform drivers that are used in organization. As we know in organizations, huge amount of data is stored into databases. In this case, use of open source distributed file system can be a better choice. It means storage data platform drivers can be used. But if we are looking for run analytics in case of online and real time applications then hybrid architecture can be considered that combined distributed file systems (Watson, Finn and Wadhwa, 2017).

Identify mode of data accessed by users and applications?

There are many NoSQL databases that have requirement for a specific application interface for accessing data. Beside this, integration of visualization or other tools that are required to access data, is also necessary. The platforms of Big Data and NoSQL are growing continuously and these are able to provide custom applications of a big data platform.

Identify of shape of data

While selecting a right application of big data, it is necessary to identify that which type of data will be managed by Big Data application. If data is unstructured or include sources streaming such as social media and video etc., then data serialization technology should use by businesses and these will help to store, capture and represent high velocity of data.

Evaluate need to integrate with existing data warehouse

If it is required by our organization to extend its current data architecture by integrating platform of big data into an existing data warehouse, then here data integration tools can be used. There are various integration data vendors exist that provide support for Big Data platforms, SQL data warehouse and data marts.

Technologies available in Big Data

With the help of above key considerations, it becomes easier to select a accurate big data application for business and to get desired results. While selecting Hadoop for healthcare industry, we have also followed above key considerations. 

Now here in this segment of report, we will discuss about technologies available in Big Data.

Technologies available in Big Data

According to analysis, we identify some essential technologies that available in Big Data that are listed as below:

• Column Oriented Databases
• Schema Less Databases
• MapReduce
• Hadoop
• Hive

Column Oriented Databases

Traditionally row oriented databases were used for online transaction processing with high speed of update data. But due to problem on short query performance with growing of data values and unstructured form of data, column oriented databases are supported now. This type of databases uses columns for storing data rather than rows. It is helpful for compression of data and quick implementation of data (MongoDB, 2017).

Schema less Databases

Under this category of database, several databases exists such as key value stores and document stores. These databases focus on the storage and accessing of huge amount of data and this data can be unstructured, semi-structured and structured (DeZyre, 2017).

MapReduce

It is a programming model and it allows for execution of complex jobs against multiple servers. There are two major tasks that included in MapReduce:

• In Map task, the dataset of input is changed into a different set of key value pairs.
• In Reduce task, Map task outcomes are integrated to form reduced set of tuples (Datamation.com, 2017).

Hadoop

Hadoop is the most popular application of Big Data. We have selected this technology of Big Data for healthcare industry. It is considered best for implementation of MapReduce and it is an open source platform for handling Big Data. Hadoop is also flexible to work with multiple data sources. This technology itself consists of several different applications such as traffic sensors that are based on a particular location and data of social media. (Safari, 2017).

Hive

It is another essential technology of Big Data. It is “SQL-like” bridge and it allows traditional BI applications to run queries against Hadoop cluster. This technology was originally developed by Facebook and it is considered as high level abstraction of Hadoop framework.

These are some important technologies of Big Data that can be used in different industries according to requirements (Yale School of Management, 2017).

As we have discussed above we deal in healthcare industry by using its one of the best technologies Hadoop, therefore, in healthcare business Big Data has great impacts that are listed as below:

1. After using Big Data in healthcare industry, it is analyzed that Big Data provides better safety practices. The quality care and patient safety is highly appreciated by Big Data. In University of California, EMR data analytics is used to create an algorithm for providing a quick warning for any kind of infection. EMR data analytics is application of Big Data (Millman, 2017).
2. For population health management, Big Data and its applications are also considered better than traditional applications. The National Institutes of Health has been working on similar strategies to expect disease outbreaks (BI Blog | Data Visualization & Analytics Blog | datapine, 2017).
3. Data security is a good impact of Big Data on healthcare industry. As we know that records of patients are the main target of cyber thieves for accessing personal information of them such as social security numbers, medicare information and credit card numbers etc. In this case, big data analytics is a good source for securing medical records by identifying changes in network (Health Catalyst, 2017).

Big data and its applications also put impact over organizations. The most important organizational impact of big data is regarding organizational change or transformation that is necessary to support and achieve the Big Data. In this case, business intelligence and data science such as data virtualization, programming and data engineering etc. are so much helpful for organizations. BI has capability to understand key business processes that are helpful to understand business reports, alerts and other business processes. At organization’s level, huge amount of structured and unstructured data is managed with the help of Big Data and its applications and it is main positive impact of Bog Data on organizations (Burgess, 2017).

Business Impact of Big Data

Conclusion

After this whole discussion we can say that Big Data technology is advanced and much better than traditional technologies that were used to store, manage and access huge amount of data. Big Data has resolved the problem of management of large amount of business data by providing large storage space and easiest way of accessing data. That is why in most of the business industries applications of Big Data are high preferred such Hadoop, Hive, MapReduce etc.

In this report, we have discussed various essential points regarding Big Data and its applications and from this discussion benefits of Big Data has cleared. Various improvements and advancements are done in Big Data technology and in future more benefits will be provided by Big Data. The CIOs and several managers of business organizations are investing in Big Data technology to get more secure storage and retrieval of data from databases. All business organizations those are using Big Data technology then no need to worry about management of their huge amount of data. But one thing is necessary to consider while implementing Big Data application in business organizations that its above discussed key considerations should be followed properly.

References

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