Difference between traditional data and big data

It has become important to create a new platform to fulfil the demand of organizations due to the challenges faced by traditional data. By leveraging the talent and collaborative efforts of the people and the resources, innovation in terms of managing massive amounts of data has become a tedious job for organisations. This can be fulfilled by implementing big data and its tools which are capable to store, analysing and processing large amounts of data at a very fast pace as compared to traditional data processing systems (Picciano 2012). Big data has become a big game-changer in today’s world. The major difference between traditional data and big data are discussed below.

Data architecture

Traditional data use centralized database architecture in which large and complex problems are solved by a single computer system. Centralised architecture is costly and ineffective to process a large amount of data. Big data is based on the distributed database architecture where a large block of data is solved by dividing it into several smaller sizes. Then the solution to a problem is computed by several different computers present in a given computer network. The computers communicate with each other in order to find the solution to a problem (Sun et al. 2014). The distributed database provides better computing, lower price and also improve performance as compared to the centralized database system. This is because centralized architecture is based on the mainframes which are not as economic as microprocessors in distributed database systems. Also, the distributed database has more computational power as compared to the centralized database system which is used to manage traditional data.

Types of data

Traditional database systems are based on structured data i.e. traditional data is stored in fixed format or fields in a file. Examples of unstructured data include Relational Database System (RDBMS) and the spreadsheets, which only answers the questions about what happened. The traditional database only provides an insight into a problem at a small level. However, in order to enhance the ability of an organization, to gain more insight into the data and also to know about metadata unstructured data is used (Fan et al. 2014). Big data uses semi-structured and unstructured data and improves the variety of the data gathered from different sources like customers, audiences or subscribers. After the collection, Bid data transforms into knowledge-based information (Parmar & Gupta 2015).

Volume of data

The traditional system database can store only a small amount of data ranging from gigabytes to terabytes. However, big data helps to store and process a large amount of data which consists of hundreds of terabytes of data or petabytes of data and beyond. The storage of a massive amount of data would reduce the overall cost for storing data and help in providing business intelligence (Polonetsky & Tene 2013).

Data schema

Big data uses the dynamic schema for data storage. Both the unstructured and structured information can be stored and any schema can be used since the schema is applied only after a query is generated. Big data is stored in raw format and then the schema is applied only when the data is to be read. This process is beneficial in preserving the information present in the data. The traditional database is based on the fixed schema which is static in nature. In a traditional database, data cannot be changed once it is saved and this is only done during write operations (Hu et al. 2014).

Data relationship

In the traditional database system relationship between the data, items can be explored easily as the number of information stored is small. However, big data contains massive or voluminous data which increase the level of difficulty in figuring out the relationship between the data items (Parmar & Gupta 2015).

Scaling

Scaling refers to the demand of the resources and servers required to carry out the computation. Big data is based on the scale-out architecture under which the distributed approaches for computing are employed with more than one server. So, the load of the computation is shared with a single application-based system. However, achieving scalability in the traditional database is very difficult because the traditional database runs on a single server and requires expensive servers to scale up (Provost & Fawcett 2013).

Higher cost of traditional data

Traditional database system requires complex and expensive hardware and software in order to manage a large amount of data.  Also moving the data from one system to another requires more hardware and software resources which increases the cost significantly. While in the case of big data as the massive amount of data is segregated between various systems, the amount of data decreases. So the use of big data is quite simple, making use of commodity hardware and open-source software to process the data (CINNER et al. 2009).

Accuracy and confidentiality

Under the traditional database system, it is very expensive to store a massive amount of data, so all the data cannot be stored. This would decrease the amount of data to be analyzed which will decrease the result’s accuracy and confidence. While in big data as the amount required to store voluminous data is lower. Therefore the data is stored in big data systems and the points of correlation are identified which would provide highly accurate results.

References

• CINNER, J.E., DAW, T. & McCLANAHAN, T.R., 2009. Factores Socioeconómicos que Afectan la Disponibilidad de Pescadores Artesanales para Abandonar una Pesquería en Declinación. Conservation Biology, 23(1), pp.124–130.
• Fan, J., Han, F. & Liu, H., 2014. Challenges of Big Data analysis. National Science Review , 1 (2 ), pp.293–314.
• Hu, H. et al., 2014. Toward Scalable Systems for Big Data Analytics: A Technology Tutorial. IEEE Access, 2, pp.652–687.
• Parmar, V. & Gupta, I., 2015. Big data analytics vs Data Mining analytics. IJITE, 3(3), pp.258–263.
• Picciano, A.G., 2012. The Evolution of Big Data and Learning Analytics in American Higher Education. Journal of Asynchronous Learning Networks, 16(3), pp.9–20.
• Polonetsky, J. & Tene, O., 2013. Privacy and Big Data: Making Ends Meet. Stanford law review, 66(25), p.11.
• Provost, F. & Fawcett, T., 2013. Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), pp.51–59.
• Sun, Y. et al., 2014. Organizing and Querying the Big Sensing Data with Event-Linked Network in the Internet of Things. International Journal of Distributed Sensor Networks, 14, p.11.
• Wielki, J., 2013. Implementation of the Big Data concept in organizations – possibilities, impediments and challenges.