International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
A Review of Multi-Model Databases: Unifying Relational, Document, and Graph Data Models KM. Anjali Kushwaha1, Deepshikha2 1Master of Technology, Computer Science and Engineering, Lucknow Institute of Technology, Lucknow, India 2Assistant Professor, Department of Computer Science and Engineering, Lucknow Institute of Technology,
Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - With the advancement of data driven
inadequacies of traditional relational systems. As a result, NoSQL databases rose to fill the gap through document, key-value, column-family, and graph data models that proved to be flexible enough in the changing technology environment. NoSQL databases were designed to resolve some specific use cases but the requirement to control many different data models inside a single application came more and more clear. Multi model databases takes a leap from here and it aims to unify different data models like relational, document and graph into one system. Multi model databases are important innovation in the database land scape which help to support the needs of modern applications, such as e commerce platforms, social networks, and IoT systems, which must co exist with structured, semi structured, and graph based data.
applications, database systems that jumped to the development of different data models efficiently were needed quickly. Over the past few years, there has been a major push to drive relational, document, and graph data models to merge into a unified system with the help of multi model databases. This review paper discusses the notion, architecture and implementation of multi-model databases in the context of being an integration platform for structured, semi-structured and graph data. We study the challenges of combining these different models together due to the heterogeneity in data representation, query languages, and performance tradeoffs. We also provide an overview of the benefits of using multi model databases as they provide increased flexibility, simplified data management, and cost efficiency but mention their disadvantages like increased complexity and performance overheads. A number of case studies are presented describing real world multi model databases with real work applications such as ArangoDB, OrientDB, and Microsoft Azure Cosmos DB. We also discuss the future trends for the following: (i) integration with AI and machine learning; (ii) further improvement on query optimization; and (iii) standardization efforts. In this paper, an attempt is made to provide a detailed insight into the multi model database, with a special focus on how it is suitable for modern data management, and how it can possibly change data storage, processing and knowledge extraction processes in the era of heterogeneous data.
Figure-1: NoSQL Databases
1.2.Problem Statement
Key Words: Multi-Model Databases, Relational Data Model, Document Data Model, Graph Data Model, Data Unification, Cross-Model Querying, Database Architecture, Query Optimization.
However, managing heterogeneous data models in separate systems is still a major challenge despite the great leap of database technologies. Organizations tend to use several database systems for the relational, document and graph data, which increases complexity, reduces the infrastructure costs and makes it difficult to keep data consistent and assured. This brings lack of a unified approach to querying and managing diverse data models and further exacerbates these challenges to lead data processing and analysis inefficient. With the increased complexity and data dependency of applications, there is a clear need of a solution that can do a constant integration and management of multiple data models in a single system.
1.INTRODUCTION 1.1.Background The history of the database is filled with important milestones, and it really started with the time of the relational databases that triumphed at keeping control of structured data, structured after the example of tables, rows, columns. But with the arrival of the internet, social media and Internet of Things (IoT), the volume, variety and velocity, in terms of data, skyrocketed revealing the
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