Comparative Case Study Difference Between Azure Cloud SQL and Mongo Atlas MongoDB NoSQL Database

Azure SQL and Atlas Mongodb NoSQL(Azure instance) databases are the most popular, systematic process to database solutions. Which Azure SQL database is also referred to as RDBMS (Relational Database Management Systems). The data are structured into tables or associations. The Atlas Mongodb NoSQL database is called a non-relational database management systems. The data are included in unstructured tables or associations. In this research, evaluate both the Azure SQL and Atlas Mongodb NoSQL databases. During the experiment compare the loading time, response time, and retrieval time of both Azure SQL and Atlas Mongodb NoSQL databases, and justify which one is fast, efficient and better performance.

Which Category Is Better: Benchmarking Relational and Graph Database Management Systems

Over decades, relational database management systems (RDBMSs) have been the first choice to manage data. Recently, due to the variety properties of big data, graph database management systems (GDBMSs) have emerged as an important complement to RDBMSs. As pointed out in the existing literature, both RDBMSs and GDBMSs are capable of managing graph data and relational data; however, the boundaries of them still remain unclear. For this reason, in this paper, we first extend a unified benchmark for RDBMSs and GDBMSs over the same datasets using the same query workload under the same metrics. We then conduct extensive experiments to evaluate them and make the following findings: (1) RDBMSs outperform GDMBSs by a substantial margin under the workloads which mainly consist of group by, sort, and aggregation operations, and their combinations; (2) GDMBSs show their superiority under the workloads that mainly consist of multi-table join, pattern match, path identification, and their combinations.

Accessing Data From Multiple Heterogeneous Distributed Database Systems

This chapter describes the method to retrieve data from multiple heterogeneous distributed relational database management systems such as MySQL, PostgreSQL, MS SQL Server, MS Access, etc. into Oracle RDBMS using Oracle's Heterogeneous Gateway Services. The complete process starting from downloading and installation of required software, creation of data source names using open database connectivity, modification of system parameter files, checking connections, creation of synonyms for tables of remote databases into oracle, creation of database links and accessing data from non-oracle databases using database links is explained in great detail. Apart from this, data manipulation in remote databases from Oracle and execution of PL/SQL procedures to manipulate data residing on remote databases is discussed with examples. Troubleshooting common errors during this process is also discussed.

Minimizing the cost by effective utilization of resources which yields better profits for government organizations in a less interval of time

Minimizing the cost and utilization of resources in an efficient manner helps any organization in its progress that is growth of the organization. This paper gives an optimized solution for reducing the cost of resources in government services of Transportation. If the data is structured can use any one of the Relational Database Management Systems (RDBMS), if the data is unstructured or semi-structured we can use hadoop for processing the data and analyze the data which is processed by using R. Strategic decision making can be made by the end results that we get from the visualizations of R-Language in-order to get better insights of the business and better decisions can be taken for better growth of the organization.

Relative Scalability of NoSQL Databases for Genotype Data Manipulation

Genotype data manipulation is one of the greatest challenges in bioinformatics and genomics mainly because of high dimensionality and unbalancing characteristics. These peculiarities explains why Relational Database Management Systems (RDBMSs), the "de facto" standard storage solution, have not been presented as the best tools for this kind of data. However, Big Data has been pushing the development of modern database systems that might be able to overcome RDBMSs deficiencies. In this context, we extended our previous works on the evaluation of relative performance among NoSQLs engines from different families, adapting the schema design in order to achieve better performance based on its conclusions, thus being able to store more SNP markers for each individual. Using Yahoo! Cloud Serving Benchmark (YCSB) benchmark framework, we assessed each database system over hypothetical SNP sequences. Results indicate that although Tarantool has the best overall throughput, MongoDB is less impacted by the increase of SNP markers per individual.