NoSQL databases refer to non-tabular files that are more advanced because they store data more conveniently than relational tables. SQL, which means Structured Query Language, was popular during the 20th century. The NoSQL database information is easier to interpret because data does not get split between tables. They allow related data to be drawn close in one data model (Yasmeen, 2018). Examples of NoSQL databases include Document databases, Key-value databases, Wide-column stores, and Graph databases. The purpose of this research paper is to find a NoSQL database that can be used in a working environment and how the organization may benefit from the application of the new model of the database. In the paper, the chosen NoSQL database is Key-value which will be explained in several ways.
The key-value of the database is a nonrelational database that applies a simple method of key-value to process and store information and data. The information in this type of database is stored in pairs of key-values in which one key is used as a distinct identifier. The keys and values are not specific items because they can be anything ranging from simple to complex. The key-value NoSQL database’s significant feature is that it is partitionable and usually permits horizontal scaling, which other models will not perform (Yasmeen, 2018). For instance, Amazon DynamoDB can allocate additional partitions to a table if the current partition is entire and extra storage is required. Therefore, Use of DynamoDB allows Amazon to benefit from autoscaling and backup options.
Amazon DynamoDB is a popular database under key-value NoSQL because it is reliable in performing at given scaling. The database is fully managed, and it is a multi-region model that offers consistency in single-digit latency. For this case, an item in DynamoDB is composed of a basic task key with a linear number of aspects to the database. The average size of an item that can be cached and associated with DynamoDB is approximately 400 KB. For the key-value NoSQL database, all tables have a specific number of items (Yasmeen, 2018). When DynamoDB is created, autoscaling is set on default to enable the inactive tables. The tool configures minimum and maximum reading capacity levels to the target authorization of variables (Yasmeen, 2018). The autoscaling applies to computing in controlling the database’s read and write metrics. The scaling creates CloudWatch alarms that are used to monitor consumed levels. The upper threshold alarm is activated from the configuration line when the reading and writing capacity breaches the aimed utilization range for about two minutes. After the upper threshold is triggered, the lower part of the point is only configured when traffic reaches the target utilization for fifteen minutes.
In DynamoDB, the read capacity units are set at 100%. The target utilization is set at 80%; therefore, autoscaling increases the provided capacity after the application goes beyond 80% for a range of two to five minutes. Autoscaling is one of the best methods because it reduces cost by over 30% if target utilization is triggered to reach the desired workload’s capacity (Yasmeen, 2018). The unique feature with autoscaling is that it adds the power of handling data quickly and, therefore, lowers cost by scaling the table’s volume responsible for reducing operational overhead.
Why Workplaces Need to Shift to NoSQL
Any workplace should move from RDBMS to NoSQL databases because it conforms to the new standards in the microservices architecture. The major reason why a modern firm should shift is that scalability and independence of functions remain achieved (Osemwegie et al., 2018). For instance, key-value NoSQL database category such as DynamoDB is useful because it can split new services automatically by configuring and triggering capacity hence providing superior performance.
If a company utilizes the new form of NoSQL models, it can develop more in terms of relational computing tasks in the working environment. By improving to NoSQL databases, a company will have the opportunity to handle comprehensive volumes of data at high speed without monolithic architecture (Osemwegie et al., 2018). A firm will have a reason to switch to the new models because there is the assurance of organized data that can be expanded more conveniently. A company will focus on having a relational database design to transform a loaded file. A firm that applies these kinds of models has a chance to have the data stored in a more accessible and closer way everyone can understand.
Many key-value databases are flexible and may be adapted by controllers and developers to new data forms. It is also advisable to use the DynamoDB model because it nests human-readable files with name and value configurations (Osemwegie et al., 2018). For example, DynamoDB has JSON files responsible for capturing high and comprehensive hierarchical data structures, which are stored in columnar databases where null figures cannot occupy big spaces compared to RDBMS. A company that incorporates the use of current key-value databases is given the liberty to change the structure of data directly. Many values are included with key-value stores without affecting the current storage of data.
Reference
Yasmeen, M. (2018). NOSQL database engines for big data management. International Journal of Trend in Scientific Research and Development, 2(6), 617-622.
Osemwegie, O., Okokpujie, K., Nkordeh, N., Ndujiuba, C., John, S., & Stanley, U. (2018). Performance benchmarking of Key-value store NoSQL databases. International Journal of Electrical and Computer Engineering (IJECE), 8(6), 5333.