Research And Evaluation On Big Data/Nosql Databases Reports Examples
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When talking about Not-Only-SQL database also known as NoSQL databases, the first impression is that it is scalable and has high performance. It was first introduced in the year 1998 and later introduced in the year 2009 when it received a new definition “No to SQL”. Designers have strategically modeled these NoSQL databases in order be more efficient when it comes to large unstructured amount of data also referred to as Big Data. Presently, most companies have opted for NoSQL databases purposely to handle Big Data issues through distributed or/and collaborated hosts (Abramova et al., 2014). For instance, a large amount of data such as LinkedIn, Face book, google and Amazon are among the ones highly implemented using NoSQL databases. However, considering the fact that there are several data security issues evolving with the introduction of new technologies such as WhatsApp, Face book and LinkedIn, NoSQL databases help in achieving PCI-DSS inform of data security enhancement.
Oracle Enterprise resource planning uses relational database management systems (RDBMS) for database managing dataflow through the e-business suite. However, RDBMS can be replaced by NoSQL databases for the purpose of enhancing sufficient data security, scalability, confidentiality and integrity. All these can be easily achieved with NoSQL databases. There are several varieties of NoSQL databases such as; graph databases, document databases, column databases and key-values databases (Bernardino et al., 2014). These four groups have different characteristics depending on the development times and the amount of data that can be stored at once. The data stored in these four data bases may also vary from types, value and quantity. Data access may also vary from using keys, fields, maps and graphs that helps to protect the data from none authorized individual as well as simplified for the users.
2.0 Graph database
In this paper, graph database is the best choice of NoSQL database that can be used since it has an element of a relational structure that can link all the base tables used in oracle e-business suite. Since oracle e-business suite is related to each other following procure to pay, order to cash, procure to capitalization and plan to budget types of modules that are used by different companies for business management. With large data and security purposes, graph data base is the best type of database for oracle e-business suite hence should be adopted by companies that are expanding or have already expanded.
Oracle often works in schemas that can be easily being incorporated in graph database that already supports relational database (Firth, 2010). Oracle as a program can be used in handling big data such as mobile clients through mobile companies, health organizations, government transactions and education records all can be kept in graph databases that can be related with other base tables in order to match the records of an individual from childhood to adulthood without much trouble. The data being stored are typified by variety, volume, complexity and data velocity that can all be managed in a graph database (Cooper et al., 2014).
2.0.1 Graph Database Functionality
Graph database uses graph theory in operation when it comes to storage data and can be used in relating tables such as invoices, requisitions, purchasing, receipts and delivery notes within an oracle platform with java interface for end user operation (Firth, 2010). End users cannot access the back end access that can only be handled by the database administrator. Graph database is the most recent database before adopting SQL (Structured query language) that is currently being used by most oracle e-business suites. Enterprise Resource planning can also be achieved by using database shading and master or slave architectures to make relational database much effective for oracle transactions (Firth, 2010).
There are various benefits of using graph databases such as; variable performance, variable scalability, high flexibility, high complexity and uses graph theory in relation the tables associated with it (Murthy et al., 2014). With several graphical regions, a standalone database can be achieved where more than 200 users can access the database at the same time from different points provided the users are able to log in to the system. With high availability, partition tolerance and strong consistency, a relational model works in a similar manner as the SQL models with a minima difference coming with query implementations (Moniruzzaman et al., 2013). It is very possible to normalize social graphs in RDBMS alongside graph network structures.
A graph network uses disks in storing the data that can also be stored in an online backup for references and access purposes. With java as an interface, integration is much easier with a flexible data structure (Neubauer, 2010). The graph size increases with increase in data stream at a constant speed of operation no matter the amount of data size stored. Hackers cannot get access to the database since there is no direct access to the back end unless one is a database administrator making it more reliable that other NoSQL databases (Neubauer, 2010).
There are tools that are used when it comes to Big Data such as Oozie, Hive and Flume. These tools help in plugging into data sources such as twitter, Facebook and Oracle among many others (Wong, 2014). Unstructured data are transferred into a distributed storage system that automatically process and organize it into query-able data tables.
Before any company implements a new technology, the first consideration is always cost management. NoSQL requires much lower cost that SQL databases more so when it comes to operational costs that are considered lower (Woodie, 2014). With lower cost commodity servers, semi-structured data can be more efficiently stored because of NoSQL scalability and flexibility. Graph databases specifically is more flexible and can accommodate any type of data provided it is well set up (Zaki, 2014). The figure 2.0 below shows the sample of Graph database used.
Figure 2.0 Graph Database sample
3.0 Challenges in storage, analysis and retrieval
There are challenges associated with graph database such as processing large data sets and retrieving the same information at the same time may take a bit longer as compared to using SQL in retrieving the same (Woodie, 2014). At some point, navigating between the tables in search of data may require some set of skills that must be implored by the user posing the same challenges on expertise (Zaki, 2014). Because of the complexity that comes along with graph database, many companies with small amount of data may still prefer to use SQL in retrieving and processing their data. But for storage purposes, NoSQL databases are much better as compared to SQL databases.
4.0 Differences of NoSQL databases
As illustrated in Appendix A, the differences between four types of NoSQL databases give a clear picture concerning graph database as compared to the others in terms of oracle e-business suite. The table 4.0 below illustrates the differences in terms of scalability, performance, flexibility, complexity, functionality and availability.
Abramova, V, Bernardino, J and Furtado, P. 2014. Which NoSQL Database? A Performance Overview. Available at: < https://www.ronpub.com/publications/OJDB-v1i2n02_Abramova.pdf >
Bernardino, J, Abramova, V and Furtado, P. 2014. Experimental evaluation of NoSQL databases. Available at: < http://airccse.org/journal/ijdms/papers/6314ijdms01.pdf>
Cooper, M and Mell, P. 2014. Tackling Big Data: NIST Information Technology Laboratory Computer Security Division. Available at: < http://bigdatawg.nist.gov/_uploadfiles/M0065_v1_4451775754.pdf >
Firth, A. 2010. Oracle ® Human Resource Management Systems: Enterprise and Workforce Management Guide Release 12.1. Available at: < http://docs.oracle.com/cd/B53825_08/current/acrobat/121hrewug.pdf>
Moniruzzaman, A.B and Hossain, S.A. 2013. NoSQL Database: Era of Databases for Big data Analytics-Classification, Characteristics and Comparison. Available at: < http://arxiv.org/ftp/arxiv/papers/1307/1307.0191.pdf >
Murthy, D and Bowman, S.A. 2014. Big Data Solutions on a small scale: Evaluating accessible high-performance computing for social research. Available at: < http://bds.sagepub.com/content/1/2/2053951714559105 >
Neubauer, P. 2010. Graph Databases, NoSQL and Neo4j. Available at: < http://www.infoq.com/articles/graph-nosql-neo4j >
Wong, M, S. 2014. Current Data Security Issues of NoSQL Databases: Network Defense & Forensic Insights. Available at: < http://www.fidelissecurity.com/files/NDFInsightsWhitePaper.pdf >
Woodie, A. 2014. Forrester Ranks the NoSQL Database Vendors. Available at: < http://www.datanami.com/2014/10/03/forrester-ranks-nosql-database-vendors/ >
Zaki, A.K. 2014. NoSQL DATABASES: New millennium database for big data, big users, cloud computing and its security challenges. Available at: < http://esatjournals.org/Volumes/IJRET/2014V03/I15/IJRET20140315080.pdf >
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Manyika, James; Chui, Michael; Bughin, Jaques; Brown, Brad; Dobbs, Richard; Roxburgh, Charles; Byers, Angela Hung .2011. Big Data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute.
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Ilya Katsov. 2012. "NoSQL Data Modeling Techniques". Ilya Katsov. Retrieved 8 May 2011.
K. Grolinger, W.A. Higashino, A. Tiwari, M.A.M. Capretz.2013. "Data management in cloud environments: NoSQL and NewSQL data stores". JoCCASA, Springer.
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