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NoSQL databases, also referred to as non-relational databases, are scalable databases that store data in a non tabular form. They offer an alternative way of data retrieval and storage than traditional databases. NoSQL databases can be used for big data and real-time web applications.
NoSQL databases offer numerous benefits compared to relational databases, such as superior performance and cost-effectiveness. In addition, they can handle large volumes of unstructured, semi-structured, and structured data and allow you to quickly scale your architecture and operations. Some of the popular NoSQL databases include Redis, Cassandra, CouchBase, CouchDB, and Neo4j.
NoSQL databases are better aligned with object-oriented programming than traditional ones and work well with modern software development methodologies. They are especially useful for version control repositories and agile projects that work on sprints. You can also easily use them for cloud projects.
Of course, they have certain drawbacks as well. For example, many NoSQL tools don’t have console access or useful management tools. They also have limited cross-platform support. Sometimes, engineers may face data integrity issues due to the lack of standardization of schemas. But that doesn’t stop companies from using them. Databases such as MongoDB, Cassandra, and Redis are some of the most common database systems used by professionals in 2021.
So how are relational and non-relational (NoSQL) databases different from each other?
1. Way of storing data
Relational databases have a straightforward way of representing and storing data. All data in a relational database is stored in tables and represented through columns and rows. You can assign specific data types for different columns, such as character, integer, or data.
NoSQL databases have a different way of storing data. There are different types of NoSQL databases such as key-value data stores (that store data in the form of binary objects and access it via keys), document stores (that store data in self-describing JSON or XML documents), graph stores (that use graphs to store, map, and query relationships between datasets) and wide-columns stores (that store data in tabular form but in column format)
2. Data Volume
A relational database can only store a medium amount of data if you use normal configurations for creating and assigning it. These databases also have a limitation to how much data you can enter in each field.
NoSQL databases can store a larger amount of data. They can also store unstructured data and even expand the database across multiple servers. That’s why NoSQL solutions are an optimal solution for distributed databases for many companies.
3. Failure
Relational databases often have a single point of failure and have low data movement velocity. As a result, even though they can efficiently retain data, they are inefficient in distribution and transfer.
NoSQL databases usually don’t have a single point of failure. As a result, they are highly durable and can efficiently transfer and distribute data across multiple nodes.
4. Transactions
Since relational databases are made upon comprehensive theoretical and mathematical models, relational databases can easily support complex transactions. In addition, relational databases focus on ACID properties for transaction control and data consistency.
NoSQL databases don’t have a standard data model, so they can’t support complex transactions. They can only support simple transactions such as single-key put and get operations on simple data models. However, NoSQL databases are currently trying to implement complex transactions, and many new products are working towards it.
5. Scalability
The relational database model is vertically scalable, i.e., you can raise the data load of your database by adding more components. However, you need to add a different server into your architecture for this.
Rational databases can’t scale horizontally because of the need to store relational data in the server along with customer data. To handle both these things, you need a lot of expensive hardware.
On the other hand, NoSQL databases are horizontally scalable, i.e., they can split their data by columns and store them into different servers. They can do that by working on simple data models that can merge shared data through simple queries. This method is much cheaper than vertical scaling.
6. Data Management
Organizations usually use relational databases to manage only structured data. Structured data requires less space and is easy to search and analyze. Furthermore, it’s stored in predefined formats, making it easy to use and analyze. But relational databases have issues indexing unstructured datasets.
NoSQL databases can manage both structured and unstructured data. Since NoSQL databases have fewer memory and category limitations (i.e., they can store data in many different forms), you can easily select the data model according to your requirements. However, mining and analyzing unstructured data is tricky and requires special tools and processes.
7. Flexibility
Relational databases don’t fit in every domain due to their inflexible data model. They need the data to be in a defined format and are usually used for datasets that are in tabular (spreadsheet-like) format. You can use relational databases for payroll management, inventory handling, and order processing systems where the schema is predefined.
NoSQL is a better fit for modern applications as it provides a flexible schema that enables you to develop programs faster. In addition, its high performance allows it to scale through qualitative data, which makes NoSQL perfect for game and mobile application development.
8. Market acceptance
Relational databases have been in the market for a long time, and working with them is very easy. In addition, there are many experts available in the market, and you can access many online resources and forums for any issues you encounter.
Even though NoSQL databases have been in use for some time, there are fewer documents and resources available for them when compared to relational databases. There is also a fewer number of experts available for NoSQL databases than relational databases.
Conclusion
NoSQL databases have recently gotten more popular due to their scalability, agile performance, and multiple processing nodes. However, they still have issues with data consistency and complex transactions.
NoSQL systems perform better in use cases that work with unsaturated data and don’t require complex transactions or queries. Conversely, relational databases triumph in use cases with structured data and complex transactions.
It’s important to note the potential and usability of both these systems. Therefore, it’s essential to constantly check your business requirements and consider the best viable solution for your business.
