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SQL vs NoSQL: Choosing a Modern Database

SQL vs. NoSQL - what are their differences, advantages and disadvantages? Let's explore these types of databases, and take a look at use cases they’re suited to.

Joel Burch

Joel Burch


Databases are the backbone of most modern software applications. The rise of cloud computing has had a significant influence on both the development and usage of database technologies. While traditional SQL-backed or relational databases have long been a standard choice, the rise of cloud computing has made NoSQL databases more prevalent. This is down to their performance and scalability. Many cloud providers offer both NoSQL and traditional SQL databases, each with features tailored to different use-cases and performance demands. This article will break down the differences between SQL and NoSQL databases, discussing their respective advantages, disadvantages, and appropriate use in various scenarios, as well as their roles within cloud-based systems.

What are SQL Databases?

SQL databases, often referred to as relational databases, have been a staple in the tech industry since the 1970s. They are characterized by their structured nature, with data organized into tables, and individual records as rows in a given table. MySQL, PostgreSQL, and Microsoft SQL Server are among some of the most widely-used SQL databases today. While each has its own specific featureset, they typically address similar workloads and manage comparable types of data.

The “SQL” acronym stands for Structured Query Language. One prominent feature of SQL databases is their adherence to ACID principles: Atomicity, Consistency, Isolation, and Durability. This ensures that any modification to the data is consistent, preventing data duplication or corruption. This is a critical feature, especially in sensitive areas like financial transactions. SQL databases also have the commonly accepted designation as having a schema. A more modern interpretation, in line with the NoSQL description in a later section, is that SQL databases enforce schema when data is written.

Originally, SQL databases were primarily hosted on physical servers on-premise. This required significant investment in hardware and continuous operational maintenance by database administrators (DBA). As cloud computing matured, the opportunity to leverage cloud services not just for storage and compute, but databases as well, allowed for more scalable and cost-effective solutions. Cloud providers began offering managed SQL database services, where they took on the responsibilities of hardware provisioning, database setup, maintenance, and scaling. Cloud-base SQL services add a variety of useful features that help SQL remain a solid choice for application databases. Native integration with other cloud services, scaling and automatic maintenance, and even providing SQL engines for more advanced columnar databases and data lakes make relational databases more than viable for modern software.

What are NoSQL Databases?

NoSQL databases emerged to tackle the limitations of SQL databases, particularly for applications requiring scalability and flexibility in how data is written and queried. Unlike their relational counterparts, NoSQL databases don't rely on tables for data storage. Instead, they forgo fixed schemas, sidestepping logic like intricate joins. It's common to hear them described as "schema-less," but a more accurate term might be "schema-on-read." This means the data is stored without a specific structure in the database, leaving it to the clients to apply a schema upon retrieval.

NoSQL databases come in a variety of types based on their data model. Some popular categories include Document databases like MongoDB, Key-Value stores such as Redis, wide-column stores like Cassandra, and graph databases such as Neo4j. For NoSQL databases, particularly ones deployed onto distributed architecture, the CAP theorem is an important tenet and a vitally important design consideration. CAP stands for: Consistency, Availability, and Partition Tolerance. The theory states that such a system can't simultaneously guarantee all three. Most distributed NoSQL deployments bias heavily towards availability and partition tolerance. This ultimately sacrifices consistency and offers what is colloquially referred to as “eventual consistency”.

While SQL and relational database technology predates generally available cloud technologies, the rise of NoSQL almost coincides with the growing popularity of cloud computing for hosting end-to-end software application architecture. As the popularity of online services grew, and the diversity of content on the web increased to match the demands of this growing user population, some of the largest sites and services had to develop new ways to process and store large amounts of unstructured data. SQL-based relational databases weren’t well suited to such volumes of data, and didn’t generally scale well to meet the fluid performance demands of web-facing applications. These providers had to develop new technologies for not only handling unstructured data, but doing so in a highly performant way under extreme loads. Providers like Amazon and Google began to publicize their efforts to handle large volumes of data, and ultimately realized there was a growing demand for such services. Over the last decade, a variety of open-source and managed service NoSQL solutions have emerged. For applications that demand high-performance and scalability with dynamic data, NoSQL is often the first choice for organizations.

Comparing SQL and NoSQL Databases

Databases form the foundation of most software applications, and picking the right one is crucial for optimal performance and future growth. The choice between SQL and NoSQL should be influenced by specific factors related to the application:

  • Data Model: How is the data structured? Is the data in a structured format with clear relationships, suitable for tables, rows, and columns, or is it heterogeneous and dynamic in its structure?

  • Business Logic: What operations will users need to be conducting most frequently? If the operations are complex and involve multiple tables, SQL's JOIN operations could be beneficial. However if there are vast volumes of unstructured data, NoSQL might be a better fit.

  • Scaling & Performance Demands: What does the current workload look like, and what are the anticipated future performance demands? Predicting organizational data needs can help pick the right database now and in the future. Cloud services may add scaling capabilities where none existed previously, such as traditional SQL-based databases.

SQL databases excel in scenarios where reliability and consistency are paramount. Their strength lies in their tried-and-tested models, which offer predictable performance and behavior. With SQL, users rarely encounter unexpected behaviors, such as hot-key issues that can be common in NoSQL environments. SQL environments can often handle most performance issues by vertically scaling: increasing the resources available for the active database nodes. However, vertical scaling does have limitations, and horizontal scaling (adding more nodes) is a much more complex endeavor.

On the other hand, NoSQL databases offer immense flexibility. They don't impose a schema when data is being written. This is vital for applications that produce a vast volume of streaming, unstructured data. For instance, consider the huge amounts of data processed and displayed by platforms like Twitter in real-time. This kind of rapid, voluminous data generation and processing would be a challenge for traditional SQL databases but is right in the wheelhouse for many NoSQL systems. Managed NoSQL services also offer automatic scaling to meet significant performance demands, without requiring significant administrative or operational overhead.

Use Case Scenarios: SQL vs NoSQL

Different applications come with unique requirements. Application data is often modeled off of business data and logical processes, which can vary significantly between organizations and industries. The choice between SQL and NoSQL databases isn’t just about technical considerations; it’s about choosing what’s the best tool to model business data.

For instance, consider a banking application. Here, data integrity and transactions are of paramount importance. Inaccuracies or losses in financial data can lead to severe repercussions, both in terms of finances and reputation. Given these needs, financial organizations should gravitate towards the consistency and safety guarantees offered by SQL databases. Their inherent transactional consistency and adherence to the ACID properties make them a natural fit for such critical applications.

A social media application paints a different picture. These platforms handle vast amounts of diverse, often unstructured data – from text updates and comments to photos, videos, and live streams. The sheer volume and variety of this data can be staggering. NoSQL databases, with their scalability and ability to efficiently manage big data, emerge as the more appropriate choice. 

Organizations looking to choose a database to support their applications should ask questions that address technical considerations and business needs:

  • Does the application's primary function involve reading data or writing data?

  • Is the projected data growth and user interaction likely to demand the scalability features of NoSQL in the foreseeable future?

  • What is the proficiency level of the current engineering team? Do they possess more expertise in SQL or NoSQL systems?

  • Lastly, what kind of data analytics does the organization intend to run? Will analytics be run concurrently within application databases, or will analytics and presentation be a separate layer? (think data warehouse)

Choose the best database for the job

Despite the hype of NoSQL databases, SQL databases offer plenty of value for organizations of all sizes. However, there are still scenarios where a NoSQL database is the best tool for the job. It's important for organizations to have a deep understanding of the specific use case they're addressing. It's not just about the technical nitty-gritty; the larger picture often encompasses business needs and workflows.

Organizations should also evaluate their team's expertise with a potential database implementation. Is the expertise and acumen present to properly manage and scale a given database? If not, how hard is it to hire for? While industry-standard solutions may not be as attention grabbing, there's a non-trivial benefit to choosing a technology for which it’s easier to find knowledgeable practitioners.

There’s also the question of cloud providers. Does the organization already have a significant investment in a particular cloud platform? If so, does that provider have a stronger offering in a particular category of database? If niche features or performance demands necessitate it, some organizations may be forced to take a multi-cloud approach, or even go with a third-party dedicated data-platform. 

Finally, understanding scaling needs now and in the future can help prevent costly refactoring and migrations down the road. Ultimately, choosing the best database for the job isn’t about the latest and greatest; it’s about what’s the best fit for the software, the engineers, and the business.

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