Relational and non-relational databases are used by businesses to store, retrieve, and analyze data. Learn how these two database types differ from each other.
Whether you’re an aspiring data professional or simply someone who must interact with data on a daily basis, you’ve probably come across the terms “relational” and “non-relational” databases. And, while both types of databases seem to have clear differences, you might not be quite sure what they are exactly – or, how they’re used.
If these are the questions you’re asking yourself, then this article is for you. Here, you’ll learn about relational and non-relational databases, find a list of common databases used today, and explore ways of deciding which database type is best for your needs. At the end, you’ll also find suggested courses to help you better understand databases and their use.
A relational database is a type of database that stores data in tables composed of rows and columns.
In a relational database, data is contained within a table, which is then linked to data contained within other tables through the use of unique identifying “keys.” Specifically, relationships between tables are formed when a “primary key,” which uniquely identifies a row in one table, connects with a “foreign key” identifying a row of data in another table.
In effect, relational databases are designed to store structured data, or well-defined data like names, dates, and quantities that can be easily standardized within a table. SQL, or Structured Query Language, is the most common language used to interface with relational databases within relational database management systems (RDMS).
Relational databases are well-suited to storing, retrieving, and manipulating well-defined, structured data. As a result, they are easy to use. Some of the most common relational databases that you can expect to encounter include:
A non-relational database is a type of database that doesn’t store data in tables but instead in whatever manner is best suited to the type of data being stored. In effect, non-relational databases are designed to contain unstructured data, or loosely-defined data like email messages, videos, images, and business documents that aren’t easily standardized. They can also be used to store a mix of structured and unstructured data.
There are several ways to create non-relational databases. Some of the most common approaches include using key-value data stores, column-family data stores, graph data stores, and document data stores. Non-relational databases are said to be NoSQL, meaning that they don’t use Structured Query Language, even though many NoSQL databases do support SQL queries.
There are many non-relational databases. Some of the most common ones you will likely encounter include:
As you’re exploring the world of data, you’ll likely encounter many unfamiliar acronyms and abbreviations that might cause you some confusion. Two such abbreviates are SQL and NoSQL. Despite their imposing names, though, these two terms are not really as complicated as they sound.
Structured query language (SQL) is the language used to interface with relational databases, which are used to house structured data. NoSQL, meanwhile, stands for “not only structured query language” and is the paradigm used for non-relational databases, which store both structured and unstructured data. In other words, a SQL database is a relational database, while a NoSQL database is a non-relational one.
Perhaps the most common question that anyone asks about relational and non-relational databases is also one of the simplest: “Which one should I use?”
To help you answer that question, here are some common considerations you should ask yourself when trying to decide the best type of database to meet your needs:
Perhaps the most important factor impacting whether you should use a relational or non-relational database is the type of data you are storing. If you’re working with structured data that is easily stored within tables, then you will likely be using a relational database. But, if you’re working with either unstructured data or a mix of both structured and unstructured data, then you will need to use a non-relational database.
Note: While a non-relational database can store structured data, a relational database cannot store unstructured data.
Another factor impacting the kind of database you decide to use is the volume of data you will be storing in your database. Typically, non-relational databases are better suited to large volumes of data than relational databases, which work most efficiently with small to medium-sized amounts of data.
If you’re working with big data or ever-growing amounts of real-time data, then you might consider using a non-relational database.
The last question you will want to consider is who will actually be using your database regularly and for what purpose.
Generally speaking, non-professionals find that relational databases are much easier to use than non-relational databases, which can require additional knowledge. However, there are some scenarios when a non-relational database might make more sense for your needs, such as if you are continuously collecting real-time business data that must also be quickly analyzed to form business insights.
The right database is the one that best fits your business and its goals, so make sure to consider these important questions before picking a database.
Databases help businesses store, retrieve, and analyze key data points in order to gain valuable insights. Prepare for a career working with data today by taking a cost-effective, online course through Coursera.
The University of Colorado Boulder’s Databases for Data Scientists Specialization teaches database design, database software fundamentals, and how to use Structured Query Language (SQL) to work with databases.
The University of California Davis’ Learn SQL Basics for Data Science Specialization, meanwhile, teaches beginners with no previous coding experience the basics of SQL, data wrangling, AB testing, and more.
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Database (DB) Design, Data Warehousing, NoSQL, database administration, SQL, Database (DBMS), Relational Models, Normalization, Entity–Relationship (E-R) Model, Data Science, Database Application, SQL (Structured Query Language), Big Data
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