Table of Contents
Nosql data models: advantages and use cases
By Malysh Igor (iimalysh@edu.hse.ru)
Introduction
NoSQL data models have gained widespread popularity due to their flexibility and scalability in handling large volumes of diverse data types. Unlike traditional relational models, NoSQL data models can be adapted to various structures such as document, key-value, column-family, and graph models, each offering unique benefits. This adaptability enables organizations to select a model that aligns with their specific data and query needs, improving performance and reducing the limitations of fixed schemas. As a result, NoSQL data models have become essential in applications that demand high availability, rapid scaling, and support for semi-structured or unstructured data [1].
This essay will explore the advantages of different NoSQL data models and highlight their most common use cases across industries.
Exploring NoSQL Data Models
The NoSQL data model is fundamentally different from the relational data models used in traditional relational database management systems (RDBMSs) because it is designed for flexibility rather than strictly defined relationships. In a relational model, data is organized into tables with rows and columns, where relationships between data are enforced using keys (such as primary and foreign keys) and schemas that describe exactly how the data should be structured.
In contrast, the NoSQL data model lacks a rigid schema, meaning it does not enforce predefined relationships or constraints between pieces of data. Instead, NoSQL data is often stored in formats such as documents, key-value pairs, wide columns, or graphs, each organized in a way that best suits the requirements of the application.
Common NoSQL data models include [2]:
- Document model: stores data as documents (such as JSON or BSON), and is used by document databases such as MongoDB
- Key-value model: represents data as key-value pairs, as in Redis
- Column-family model: stores data in columns and column families, commonly used in databases such as Cassandra
- Graph model: represents data as nodes, edges, and relationships, suitable for graph databases such as Neo4j
Advantages of Nosql data models
The previous section describes the data models used in NoSQL databases. The rationale for using these models can be described using the CAP theorem, which explains the tradeoffs in distributed database systems between three properties: consistency, availability, and partition tolerance [3].
Consistency means that all nodes see the same data at the same time, ensuring that every read reflects the latest write. Availability means that every request gets a response, even if some nodes are offline. Partition tolerance means that the system continues to operate despite network failures that separate nodes.
Below is also a figure illustrating the CAP theorem with databases that “choose” CA, CP, and AP.
Fig. 1 [10]
As you can see in the figure, a relational database can only provide the “CA” (Consistency and Availability) part of the diagram. This means that while relational databases support strong consistency and high availability, they are not designed to handle partition tolerance, making them less suitable for distributed systems where network failures are common [4].
Based on the information provided, NoSQL data models offer several advantages, making them ideal for dynamic and large-scale applications [5].
The NoSQL approach is flexible, meaning it allows data to be stored without a rigid schema, enabling easy grouping of related data without normalization. This adaptability is valuable for startups where data requirements evolve during development, making it difficult to define a final structure from the outset.
Data availability is another strength, as NoSQL databases leverage partition tolerance through replication and sharding, ensuring that data remains accessible even if some nodes are down. This redundancy provides high fault tolerance, with data replicas automatically updated as nodes come back online.
Scalability is also enhanced in NoSQL models, where data can be distributed across multiple servers, allowing for cost-effective horizontal scaling as data grows, unlike the more costly vertical scaling needed in SQL databases.
Additionally, high performance is achieved through optimized data structures that eliminate the need for complex JOIN operations, speeding up data retrieval, analytics, and aggregations.
Finally, NoSQL databases support resource savings through horizontal scaling, reduced hardware costs, and open-source options, with cloud compatibility that further enhances flexibility and cost efficiency.
Also, NoSQL models make it easier to design storage in ways that align closely with the needs of developers [6]. Unlike traditional relational databases, NoSQL databases use formats like JSON, which resemble code structures and give developers more control over data organization. Because NoSQL databases store data in structures similar to application data objects, less transformation is needed when reading from or writing to the database. This native storage capability allows developers to store data “as is,” eliminating the need for extensive ETL processes to fit data into rigid row-and-column structures. As a result, developers can streamline the setup of new databases without requiring additional tools to manage or adapt data formats.
Nosql data model use cases
In a fast-changing world where businesses and organizations must innovate quickly, flexibility and scalability are critical. NoSQL databases provide the flexibility of dynamic schemas and support a variety of data models, making them ideal for applications that require large volumes of data and low-latency responses. This makes NoSQL an ideal choice for industries, where fast response times and adaptability are critical [7].
NoSQL databases are well-suited for applications that require real-time data analysis, such as social media monitoring, ad targeting, and personalized content recommendations. They are used to collect web analytics data in real time using a solution called Clickhouse.
IoT applications often generate vast amounts of semi-structured or unstructured data that need to be processed quickly. NoSQL databases support the distributed architecture and high scalability required to handle data from millions of connected devices, providing reliable storage and processing even as data continuously flows in [8].
NoSQL databases can help e-commerce companies manage large volumes of data, including product catalogs, customer profiles, and transaction histories. NoSQL databases are also capable of handling high traffic volumes, making them an excellent choice for e-commerce applications that experience surges in demand. E.g. Airbnb uses NoSQL databases to store and manage data for its booking platform, including property listings, guest profiles, and booking histories.
Social media platforms need to manage large amounts of semi-structured data, including posts, comments, likes, and connections [9]. NoSQL databases can store user data flexibly and scale as the network grows, ensuring quick access and updates to user interactions and profiles. E.g. Netflix uses NoSQL databases to store and manage massive amounts of data, including customer profiles, viewing histories, and content recommendations. NoSQL databases allow Netflix to handle large volumes of data and provide fast, reliable access to data across a distributed network.
Conclusion
In conclusion, NoSQL data models represent a transformative shift in how organizations manage and interact with data, offering significant advantages in flexibility, scalability, and performance. The adaptability of NoSQL databases enables organizations to choose the most suitable model for their specific requirements, facilitating efficient data management in dynamic environments. We discussed how industries such as e-commerce, social media and IoT leverage NoSQL for rapid response times and real-time data processing. In summary, the advantages of NoSQL data models, including their ability to accommodate large volumes of semi-structured and unstructured data, make them an essential choice for modern applications, ensuring organizations can thrive in an ever-evolving digital landscape.
References
- ChatGPT 4o, what is the difference between nosql data models and nosql database? Also give me a quick overview of the topic https://chatgpt.com/
- Lourenço, J.R., Cabral, B., Carreiro, P. et al. Choosing the right NoSQL database for the job: a quality attribute evaluation. Journal of Big Data 2, 18 (2015). https://doi.org/10.1186/s40537-015-0025-0
- ChatGPT 4o, Please give me more details about the CAP theorem, use info from image https://chatgpt.com/
- ChatGPT 4o, Provide me use cases for nosql data models https://chatgpt.com/
- CAP theorem with databases that “choose” CA, CP and AP https://journalofbigdata.springeropen.com/articles/10.1186/s40537-015-0025-0/figures/1