RDBMS vs. NoSQL: What’s the difference?

The modern enterprise relies heavily on databases to power core decision-making for applications, such as enterprise resource planning (ERP) systems, online payments, and social media. The database industry has undergone significant changes to cope with evolving business needs.

Relational database management systems (RDBMS), also called relational databases, and NoSQL databases represent the most common types of databases used today. In relational databases, data is stored in tables and follows a strict predefined schema. On the other hand, NoSQL does away with the predefined schema, allowing multi-dimensional data records to be kept without a fixed schema. Let’s explore the key characteristics of these two kinds of databases and their major strengths and weaknesses.

RDBMS strengths and weaknesses

For several decades now, relational databases have been crucial to modeling real-world data. With a rigid structure of rows and columns and normalized datasets, relational databases are ideal for handling data models that are well understood and do not change often.

RDBMS uses structured query language (SQL), which is based on the ANSI/ISO standard. There are different implementations of this standard by different vendors and at a high-level support some basic commands to perform read, write, update, and delete operations on relational data. The SQL language has a declarative syntax that is concise and easy to understand, making it a popular choice among application developers.

Strengths

• ACID guarantees ACID guarantees ensure that database transactions will either succeed or fail in an expected way. Relational databases provide ACID guarantees for each database transaction, which makes them ideal for data that needs to be reliably recorded, like financial transactions, inventory logs, catalogs, and other essential business data.

• Vertical scalability Vertical scaling (scaling-up) refers to adding more compute resources like CPUs and memory to your database server to increase your application traffic. Since relational databases have monolithic architectures, they are designed to run on a single server. Adding more hardware capacity to the server at hand makes it possible to scale vertically without requiring any changes to the application code.

Weaknesses

• Lack of flexibility A relational database works well when the application has attributes predefined based on the schema — storing data in two-dimensional tables with rows and columns. However, if the application schema changes frequently, the relational database may not be flexible enough to meet these dynamically evolving application requirements.

• Scale limits Not only does vertical scaling increase hardware costs, but it also has an upper limit on the hardware capacity (maximum CPU cores, maximum RAM), beyond which you cannot scale. Further, the servers need to be taken down for upgrading hardware, impacting the availability of mission-critical applications.

When are NoSQL databases a better choice?

Data today is dispersed, diverse, and it comes in multiple rich formats. With the advent of intelligent apps based on Web2.0, IoT, and AI technologies, there has been an explosion in the amount of data that needs to be processed by Engineering teams. According to Market Research Media, NoSQL databases are expected to grow more than 20 percent annually to reach $3.4 billion by 2024.

NoSQL databases are schemaless, with a data model that can dynamically adapt to the changing application context. To meet these requirements, data is stored in JSON documents, key-value pairs, wide data columns, and graphs. This schema flexibility and a variety of formats make NoSQL databases ideal for companies dealing with changing data requirements and different kinds of unstructured data.

Strengths

• Agility in development Instead of getting bogged down by defining a schema upfront, developers can work with loosely defined datasets and change them later as per the feature iterations. In addition, NoSQL databases can store data in JSON document format, allowing developers to easily read and write data and integrate it with modern web applications. This can improve agility in the overall software development cycle.

• Horizontal scalability Scaling out horizontally means increasing server resources to handle the traffic. To achieve this, teams can add servers and shard the data — that is, divide the data into subsets and distribute them among different servers. With a NoSQL database, horizontal scaling is typically less expensive since the data is already packed in self-contained objects, making it easier to migrate to other servers.

Weaknesses

• Not ACID-compliant While NoSQL databases are well designed to scale horizontally, it provides that scalability at the expense of reliability. NoSQL databases cannot guarantee ACID transactions without adding significant overhead to the database cluster.

• Complexity in scaling When multiple servers are involved, sharding a database becomes a complex process. With every new node added to the database cluster, the complexity increases — you will need to configure the load balancer to be aware of this new server, tune network communication, and put in additional monitoring stacks and tools like Grafana and Apache Zookeeper. Indeed, all of this is needed when you have a large dataset, but it may not be worth the effort when the dataset is small.

Get the flexibility of NoSQL with the reliability of an RDBMS

Each kind of database, be it relational or NoSQL, has its own set of strengths and weaknesses depending on the use case it is used for. Wouldn't it be great to have the best of both worlds?

Fauna is a flexible, developer-friendly, ACID-compliant document database delivered as a secure and scalable cloud API. It provides the agility of NoSQL and the reliability of an RDBMS without the operational headaches associated with managing your database infrastructure. The serverless technology of Fauna also ensures that your database capacity and bandwidth grow with your application, with no downtime or wasted cycles.

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