NoSQL vs. SQL: Five Key Differences

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NoSQL and SQL are the two primary forms of database used to store and manage digital data, with each providing key differences that support advantages and disadvantages. SQL deals with relational databases and NoSQL deals with non-relational databases. Both methods store data effectively but differ dramatically in their scalability, relationships, language, and database design. Understanding the differences between NoSQL vs. SQL databases can help in choosing the most appropriate technology for an organization’s specific needs.

For example, when building an app, it is important to select the most appropriate database technology for your needs (and budget). App developers would normally select a NoSQL database if they require massive amounts of throughput (data passing through the system). However, developers who need data accuracy and ACID (Atomicity, Consistency, Isolation, and Durability compliance for their applications would choose a relational database.

These two models have evolved significantly over the last few decades. Steadily increasing volumes of data (particularly unstructured data) have been an important feature in their development. Increased storage capacity and processing power, as well as expanding the use of analytics, have prompted an interest in these two fundamentally different database technologies.

SQL and NoSQL are both mature database designs, with a variety of tools and supporting software.

SQL, a structured query “language,” as a concept, was first developed in the 1970s by Edgar Codd. Structured query languages work with relational databases, and are fairly easy to learn. Codd’s design of using columns and rows was a major innovation at the time, making computers accessible to people lacking a computer science degree.

NoSQL, as a concept, was developed in 1998 by Carl Strozz. It came about in response to the problems people were having with web data – unstructured data and a desire for faster processing. (NoSQL is commonly translated to mean “Not only SQL.”) Rather than using SQL to organize and retrieve data, it uses JSON, YAML, XML, or binary schemas. NoSQL was quickly picked up by Amazon, Facebook, and Google, which were trying to cope with massive volumes of data. 

Differences Between SQL and NoSQL

The primary differences between NoSQL and SQL databases are the way they are built, how data is stored and located, and the structures and formats they use. A comparison of the two based on features, such as speed, limitations, and availability can be used to determine the type of database that best meets your needs. Additionally, consider:

1. Different Architectural Designs – Business and Research

An SQL (relational) database is ideal for dealing with business models that do not change often, require adherence to strict international standards, and want to record standardized business transactions. Relational models are designed to deal with both data files and the data’s relationships in uniform ways. A strength of relational data models is their use of a common, unified language. 

Non-relational (NoSQL) databases are ideal for organizations dealing with constantly changing data requirements, high traffic volume, and a variety of data types. NoSQL databases do not establish relationships. NoSQL systems don’t rely on, nor can they support, joined tables. Instead, there are several different schema designs, ranging from key-value to graph databases. Some NoSQL systems will support “multi-model” schemas, which means they can operate with more than one type of data schema.

Relational data models are designed to support business processes, internet communications, and research with limited amounts of data. Non-relational databases are designed primarily for researching vast amounts of data (formerly known as big data, until big data became a normal amount of data), and can be adapted to perform business transactions. 

2. Data Storage

SQL databases typically offer limited data storage – more than enough to store business records, but not enough for research that includes massive amounts of data taken from external sources. In SQL, each record is stored using a fixed-sized data page contained within a larger file. A data page will have multiple rows of data, based on the row size. Data pages will typically support eight kilobytes, although their actual size can vary.  

NoSQL models use a distributed database system – a system with multiple computers. Data storage is designed to expand horizontally through the use of additional computers. This design theoretically allows for unlimited data storage and makes NoSQL databases a better choice for working with large amounts of data.

The centralized database design and structured nature of SQL storage systems places limits on the amount of data they can store. NoSQL is an excellent system for storing massive amounts of data.

3. Data Formats

Data formats can be described as the rules defining how databases store and display data. These rules can be applied to differing data types, for example, dates, numbers, strings, and binary data. Data formats impact how data is sorted, filtered, and aggregated. 

Examples of data formats include PDF, XML, RTF SPSS, jpg / jpeg, gif, png, tiff, mpeg, mp4, Stata, Excel, QuickTime, AVRO, CSV, JSON, Protocol Buffers, and ORC.

4. Security 

There are a wide variety of SQL databases, and many have robust security features, including encryption and authentication, while others do not. There are a variety of data security tools available. 

NoSQL databases sometimes have weaker security systems than SQL databases. Often, they lack built-in features that are used for data encryption or user authentication. (There are NoSQL databases that can support and use the security features of SQL databases.)

5. ‍Flexibility

NoSQL databases offer greater storage flexibility than SQL databases because they allow for the storage of different types of data in one place. This makes them ideal for applications that require more complex data structures. Additionally, NoSQL databases are designed to scale quickly and easily, making them suitable for large-scale projects.

While SQL does not offer the same flexibility as NoSQL in terms of storing different types of data, it does offer greater flexibility in the number of business-oriented software programs that it is compatible with.

SQL Use Cases

Relational databases using SQL are popular because they are easy to understand and use. This is because they are designed to accept commands represented by simple English words and terms, and because data can be accessed directly from where it is stored, rather than copying it to other applications. SQL databases can support the activities listed below, and many more.

  • Marketing: First-party data is becoming more important for marketing purposes. First-party data (data collected directly from the customer) is typically a smaller amount of data than third-party data (purchased from outside resources) and is often stored in-house. As third-party data becomes less and less available, first-party data is being used for marketing. The organization of relationships an SQL database provides for first-party data can be very useful for marketing purposes. SQL can assist marketing teams in targeting customers and releasing promotions that are based on customer data that has been collected directly by the organization. 
  • Finance: Businesses often store their financial data in databases, because they are considered secure and can be accessed easily. This data can be examined when making data-driven decisions. Quarterly sales data can be analyzed to develop predictions for the upcoming year. Additionally, slow sales periods can be identified with the goal of preventing them from happening again.
  • Healthcare: SQL is now being used to analyze patient information that has been stored in clinical databases. SQL can easily manage electronic health records, allowing healthcare providers to retrieve and update their patients’ records, and generate reports on a patient. SQL in healthcare can be used to perform healthcare analytics, and monitor a patient’s data and identify patterns that may require intervention.

NoSQL Use Cases

NoSQL offers a solution for the scale and flexibility challenges businesses face in research and dealing with vast amounts of rapidly changing data. It can be used for the internet of things, social networks, real-time analytics, and gathering business intelligence. Aside from basic large-scale research, NoSQL databases can also support the efforts listed below, and several others.

  • Internet of Things: Currently, billions of devices – ranging from smartphones, Fitbits, and home appliances to systems installed in factories, hospitals, and cars – are connected with the internet and processed by NoSQL databases. These devices receive data about the environment, the device’s location, a person’s heartbeat, the movement of products, etc. This data can be used by organizations in retail, manufacturing, healthcare, and many other fields.
  • Real-Time Analytics: NoSQL’s ability to extract useful data in real time has become a necessity for modern data-driven businesses. Real-time snalytics can be used to increase operational efficiency, lower costs, and increase profits, by reacting to “current data,” immediately. NoSQL is designed for real-time analytics.
  • Content Management: It has become a necessity for modern businesses to engage their users and customers with rich, informative content. This content, however, is not limited to text or structured data. Content management also includes many forms of semi- and unstructured data – such as audio, video, images, presentations, and much more. Organizations dealing with a wide variety of data formats could find NoSQL databases to be quite useful. 

The Future of SQL and NoSQL: NewSQL

Although NoSQL is trending and gaining popularity, it cannot be used as a replacement for SQL. SQL databases have become entrenched in daily business activities and provide a uniform norm. Relational databases should remain a mainstay of businesses for decades to come.

NoSQL, on the other hand, is still evolving and finding new uses. Although it is a mature technology, its development has taken a variety of directions and is not standardized. 

One solution for combining SQL and NoSQL is called NewSQL. NewSQL databases attempt to merge the benefits of both NoSQL and SQL databases into one platform. NewSQL is still a relatively new concept, and still evolving. As with NoSQL, NewSQL databases have a wide variety of architectural models, with different features and functionalities. 

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