In this paper we will try to present a comparative study of non-relational databases and relational databases. We mainly focus our presentation on one implementation of the NoSQL database technology, namely MongoDB, and make a comparison with another implementation of relational databases, namely MySQL, and thus justifying why MongoDB is more efficient than MySQL. We will also present the advantages of using a non-relational database compared to a relational database, integrated in a forum in the field of personal and professional development. The NoSQL database used to develop the forum is MongoDB, and was chosen from a variety of non-relational databases, thanks to some aspects that we will highlight in this article. The database integration in the framework will also be presented.

In the current context of emerging several types of database systems (relational and non-relational), choosing the type and database system for storing large amounts of data in today’s big data applications has become an important challenge. In this paper, we aimed to provide a comparative evaluation of two popular open-source database management systems (DBMSs): MySQL as a relational DBMS and, more recently, as a non-relational DBMS, and CouchDB as a non-relational DBMS. This comparison was based on performance evaluation of CRUD (CREATE, READ, UPDATE, DELETE) operations for different amounts of data to show how these two databases could be modeled and used in an application and highlight the differences in the response time and complexity. The main objective of the paper was to make a comparative analysis of the impact that each specific DBMS has on application performance when carrying out CRUD requests. To perform the analysis and to ensure the consistency of tests, two similar applications were developed in Java, one using MySQL and the other one using CouchDB database; these applications were further used to evaluate the time responses for each database technology on the same CRUD operations on the database. Finally, a comprehensive discussion based on the results of the analysis was performed that centered on the results obtained and several conclusions were revealed. Advantages and drawbacks for each DBMS are outlined to support a decision for choosing a specific type of DBMS that could be used in a big data application.

Databases are an important part of today’s applications where large amounts of data need to be stored, processed, and accessed quickly. One of the important criteria when choosing to use a database technology is its data processing performance. In this paper, some methods for optimizing the database structure and queries were applied on two popular open-source database management systems: MySQL as a relational DBMS, and document-based MySQL as a non-relational DBMS. The main objective of this paper was to conduct a comparative analysis of the impact that the proposed optimization methods have on each specific DBMS when carrying out CRUD (CREATE, READ, UPDATE, DELETE) requests. To perform the analysis and performance evaluation of CRUD operations for different amounts of data, a case study testing architecture based on Java was developed and used to show how the databases’ proposed optimization methods can influence the performance of the application, and to highlight the differences in response time and complexity. The results obtained show the degree to which the proposed optimization methods contributed to the application’s performance improvement in the case of both databases; based on these, a detailed analysis and several conclusions are presented to support a decision for choosing a specific approach.

In the context of the heavy demands of Big Data, software developers have also begun to consider NoSQL data storage solutions. One of the important criteria when choosing a NoSQL database for an application is its performance in terms of speed of data accessing and processing, including response times to the most important CRUD operations (CREATE, READ, UPDATE, DELETE). In this paper, the behavior of two of the major document-based NoSQL databases, MongoDB and document-based MySQL, was analyzed in terms of the complexity and performance of CRUD operations, especially in query operations. The main objective of the paper is to make a comparative analysis of the impact that each specific database has on application performance when realizing CRUD requests. To perform this analysis, a case-study application was developed using the two document-based MongoDB and MySQL databases, which aim to model and streamline the activity of service providers that use a lot of data. The results obtained demonstrate the performance of both databases for different volumes of data; based on these, a detailed analysis and several conclusions were presented to support a decision for choosing an appropriate solution that could be used in a big-data application.

The purpose of this paper is to present a comparative study between relational and non-relational database models in a web-based application, by executing various operations on both relational and on non-relational databases thus highlighting the results obtained during performance comparison tests. The study was based on the implementation of a web-based application for population records. For the non-relational database, we used MongoDB and for the relational database, we used MSSQL 2014. We will also present the advantages of using a non-relational database compared to a relational database integrated in a web-based application, which needs to manipulate a big amount of data.

Data exchange between different devices and applications has become a necessity nowadays. Data is no longer stored locally on the device, but in the cloud. In order to communicate with the cloud and exchange data, web services are being used. To keep the communication consistent across different devices and platforms, the data needs to be formatted using a standard data format, such as JSON or XML. This paper compares both standards and provides an in depth analysis of their performance. In order to perform the analysis a web API was built in the PHP framework Laravel, which was then tested with the help of the API development environment called Postman for different number of transferred items.