Migration from a Relational Database to NoSQL

2018 ◽  
Vol 8 (3) ◽  
pp. 63-80
Author(s):  
Samah Bouamama
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
New Technologies ◽  
Management Tools ◽  
Nosql Databases

This article describes how due to the monstrous evolution of the technology and the enormous increase in data, it becomes difficult to work with traditional database management tools; relational databases quickly reach their limits and adding servers does not increase performance. As a result of this problem, new technologies have emerged, such as NoSQL databases, which radically change the architecture of the databases that the authors are used to seeing, thus increasing the performance and availability of services. As these technologies are relatively new, standard or formal migration processes do not yet exist, the authors thought it useful to propose a migration approach from a relational database to a database-oriented columns type HBase and Cassandra.

scholarly journals Volume-Adaptive Big Data Model for Relational Databases

2021 ◽  
Vol 10 (3) ◽  
pp. 2283-2295
Keyword(s):  
Big Data ◽  
Distributed Systems ◽  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
Data Partitioning ◽  
Database Management System ◽  
Np Hard ◽  
Related Data ◽  
Partitioning Strategy

Big data is traditionally associated with distributed systems and this is understandable given that the volume dimension of Big Data appears to be best accommodated by the continuous addition of resources over a distributed network rather than the continuous upgrade of a central storage resource. Based on this implementation context, non- distributed relational database models are considered volume-inefficient and a departure from their usage contemplated by the database community. Distributed systems depend on data partitioning to determine chunks of related data and where in storage they can be accommodated. In existing Database Management Systems (DBMS), data partitioning is automated which in the opinion of this paper does not give the best results since partitioning is an NP-hard problem in terms of algorithmic time complexity. The NP-hardness is shown to be reduced by a partitioning strategy that relies on the discretion of the programmer which is more effective and flexible though requires extra coding effort. NP-hard problems are solved more effectively by a combination of discretion rather than full automation. In this paper, the partitioning process is reviewed and a programmer-based partitioning strategy implemented for an application with a relational DBMS backend. By doing this, the relational DBMS is made adaptive in the volume dimension of big data. The ACID properties (atomicity, consistency, isolation, and durability) of the relational database model which constitutes a major attraction especially for applications that process transactions is thus harnessed. On a more general note, the results of this research suggest that databases can be made adaptive in the areas of their weaknesses as a one-size-fits- all database management system may no longer be feasible.

scholarly journals Trends and Challenges of Databases

2018 ◽  
Vol 3 (5) ◽  
pp. 71-75
Author(s):  
Mária Princz
Keyword(s):  
High School ◽  
High Schools ◽  
Data Processing ◽  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
Data Retrieval ◽  
Processing Data ◽  
Relational Database Management

The database management, using relational databases, is part of curriculum in the Hungarian high schools. The aim of this paper is to present how we can show for students the challenges facing data processing, data retrieval, beyond the relational database management taught in high school.

Which Way to Go for the Future

2018 ◽  
pp. 311-328
Keyword(s):  
Big Data ◽  
Relational Database ◽  
Data Model ◽  
Driving Force ◽  
Relational Databases ◽  
High Availability ◽  
Nosql Databases ◽  
The Future ◽  
Data Requirements ◽  
Open Issues

The chapter presents how relational databases answer to typical NoSQL features, and, vice versa, how NoSQL databases answer to typical relational features. Open issues related to the integration of relational and NoSQL databases, as well as next database generation features are discussed. The big relational database vendors have continuously worked to incorporate NoSQL features into their databases, as well as NoSQL vendors are trying to make their products more like relational databases. The convergence of these two groups of databases has been a driving force in the evolution of database market, in establishing a new level of focus to resolving big data requirements, and in enabling users to fully use data potential, wherever data is stored, in relational or NoSQL databases. In turn, the database of choice in the future will likely be one that provides the best of both worlds: flexible data model, high availability, and enterprise reliability.

scholarly journals Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zain Aftab ◽  
Waheed Iqbal ◽  
Khaled Mohamad Almustafa ◽  
Faisal Bukhari ◽  
Muhammad Abdullah
Keyword(s):  
Relational Database ◽  
Business Intelligence ◽  
Relational Databases ◽  
State Of The Art ◽  
Schema Mapping ◽  
Excellent Performance ◽  
Nosql Databases ◽  
Nosql Database ◽  
Domain Expertise ◽  
Database Transformation

Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

scholarly journals Automated Algorithm for Data Migration from Relational to NoSQL Databases

2018 ◽  
Vol 21 (1) ◽  
pp. 60
Author(s):  
Alza A. Mahmood
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Database Management System ◽  
Data Migration ◽  
Management Systems ◽  
Structure Form ◽  
Nosql Databases ◽  
New Approach ◽  
New Class ◽  
Automated Algorithm

   One of the barriers that the developer community face once turning to the newly, highly distributable, schema agnostic and non-relational database, called NoSQL, which is how to migrate their legacy relational database (which is already filled with a large amount of data) into this new class of database management systems. This paper presents a new approach for converting the already filled relational database of any database management system to any type of NoSQL databases in the most optimized data structure form without bothering of specifying the schema of tables and relations between them. In addition, a simplified software as a prototype based on this algorithm is built to show the results of the output for testing the validity of the algorithm.

The Berlin SPARQL Benchmark

2011 ◽  
pp. 81-103 ◽  
Author(s):  
Christian Bizer ◽  
Andreas Schultz
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
Query Language ◽  
Storage Systems ◽  
Sparql Query ◽  
Use Case ◽  
Benchmark Experiment ◽  
Relational Database Management ◽  
Relational Database Management Systems

The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).

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

2021 ◽  
Vol 9 (7) ◽  
pp. 999-1002
Keyword(s):  
Relational Database ◽  
Database Management ◽  
Database Management Systems ◽  
Management Systems ◽  
Comparative Case Study ◽  
Nosql Databases ◽  
Nosql Database ◽  
Relational Database Management ◽  
Relational Database Management Systems

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.

Towards a Fuzzy Object-Relational Database Model

2011 ◽  
pp. 435-461 ◽  
Author(s):  
Carlos D. Barranco ◽  
Jesús R. Campaña ◽  
Juan M. Medina
Keyword(s):  
Relational Database ◽  
Management System ◽  
Database Management ◽  
Relational Databases ◽  
Database Management System ◽  
Data Types ◽  
Relational Database Management System ◽  
Database Model ◽  
Object Relational ◽  
Relational Database Management

This chapter introduces a fuzzy object-relational database model including fuzzy extensions of the basic object-relational databases constructs, the user-defined data types, and the collection types. The fuzzy extensions of these constructs focus on two main flexible aspects, a way to flexibly compare complex data types and an extension of collection types allowing partial membership of its elements. Collection operators are also adapted to consider flexibly comparable domains for its elements. Such a fuzzy object-relational database model, and its implementation in a fuzzy object-relational database management system, provides an easy and effective way to manage a great amount of complex fuzzy data in object-relational databases for emerging fuzzy applications. As a sample of the proposal advantages, an application for dominant color based image retrieval, which is built on an object-relational database management system implementing the proposed fuzzy database model, is introduced.

scholarly journals CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM

2019 ◽  
Vol 7 (7) ◽  
pp. 351-359
Author(s):  
Yashraj Sharma ◽  
Yashasvi Sharma
Keyword(s):  
Relational Database ◽  
System Architecture ◽  
Relational Databases ◽  
Database System ◽  
Database Management System ◽  
Nosql Databases ◽  
Advantages And Disadvantages ◽  
Relational Database System ◽  
Big Data Applications ◽  
Nosql Database

On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

scholarly journals Storage organization for "open" sets of entity attributes in relational databases

2021 ◽  
Vol 12 (5-2021) ◽  
pp. 128-139
Author(s):  
Andrey G. Oleynik ◽  
Keyword(s):  
Information Systems ◽  
Relational Database ◽  
Database Management ◽  
Relational Databases ◽  
Database Management Systems ◽  
Management Systems ◽  
Two Dimensional ◽  
Open Set ◽  
Special Relations

Relations are practically implemented by database management systems in the form of two-dimensional tables. In this regard, certain difficulties arise in the development of relational database schemas, in which it is necessary to represent objects with an alterable (open) set of attributes. The article proposes a solution to this problem by including special relations in the scheme - relations of properties directory. Properties directory allow replenishing the sets of object attributes without changing the structure of the database. Examples of the practical use of properties directory in the development of database schemas of two information systems are presented.

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