When looking at relational vs non-relational databases, keep in mind that one is not better than the other, merely different. Specifically, a relational database stores data in rows and tables and links information as needed. A non-relational database, on the other hand, uses a storage model that is geared toward the type of data being stored.
A relational database is also known as Relational Database Management System (RDBMS) or SQL database (with Structured Query Language, or SQL, being the means with which to access data in a relational database). In 1970, E.F. Codd at IBM’s San Jose Research Lab published a paper showing how information stored in large databases could be accessed without knowing how that information was structured or where it resided in the database. He used the term “relational database.”
Codd pointed out that it was no longer required that computer experts were necessary to retrieve specific information, which was time-consuming and costly. Instead, different database users would be able to use these databases as needed.
In 1998, Carlo Strozzi first used the acronym “NoSQL” when naming his database, which did not use SQL. NoSQL began being used to describe non-relational databases or “not only SQL,” which means systems can support SQL-like query languages. The NoSQL database was developed in response to a need for processing unstructured data and for faster processing.
There are different types of non-relational databases. Specifically, a document database allows documents to be stored and retrieved in a form that is much nearer to the data objects used in applications. Such databases are considered more flexible in that they allow developers to rework document structures as needed. Key-value stores have a key and a value that is used to store every data element in the database. These are viewed as the simplest type of NoSQL database. A column-oriented database is organized as a set of columns rather than by row. With a graph database, elements are stored as a node, and connections between elements are known as links or relationships. A wide-column database organizes data storage into flexible columns that can be spread across multiple servers or database nodes. A time series database is designed to retrieve and store data records that are part of a data set that are associated with time stamps.
What relational databases do well
There are several benefits to a relational database, such as:
- Simplicity. With a relational database, there are no tiresome architectural processes like hierarchical database structuring or definition. The simple structure can be handled with SQL queries and doesn’t entail complex queries.
- Consistency. A relational database with its arranged tables allows data to be administered and operated in different ways without having to rearrange the entire set of tables. SQL queries are applied for interactive queries that retrieve information and gather data for reporting or analysis. One example of a relational database is online banking. A customer can make a deposit via an ATM, and then see that transaction is completed through a personal smartphone. The data is consistent no matter where it is viewed because of the relational model.
- Accuracy. Primary key and foreign key concepts used in a relational database mean that multiple tables can be related to one another. This ensures that the data is non-repetitive and prevents data duplication.
- Reliability. A standard known as ACID compliance (atomicity, consistency, isolation, and durability) guarantees database transaction reliability. For example, if one change fails, then the whole transaction fails. This leads to the database remaining unchanged, which can be critical in areas such as banking.
- Flexibility. More data, updates, and deletions can be handled well by relational databases. There is no limit to the number of rows, columns, or tables.
- Normalization. Normalization is the process of organizing the data in a database. A set of regulations, characteristics, and purposes for the database structure are part of a normalization process. This can help to protect the data and make the database more flexible because it eliminates redundancy and inconsistencies.
- Security. Boundaries can be set so that only some personnel can access certain tables. This can be critical for banking and finance, but also to protect corporate information from being released to the public. In addition, data can be kept secure even if the network and server are compromised.
What non-relational databases do well
When looking at non-relational databases, or NoSQL databases, consider these benefits such as:
- Low latency. Because the data can be stored in ways that are easier to understand, an organization can gain faster access to its data, a critical component in a real-time economy.
- Availability. Non-relational databases focus on making data available as real-time use cases demand instant access. They accomplish this by creating multiple copies, so if one copy is not available, another copy can be accessed. The tradeoff is guaranteeing that the copies of the data are consistent with one another when one copy becomes unavailable (although this is not always the case).
- Flexibility. There are minimal structure limitations with a non-relational database. The NoSQL database allows data retrieval and storage with little or no requirements for predefined schema. Non-relational databases can be a great fit for applications such as mobile, web, and gaming because they require flexible, high-performance databases to provide first-rate user experiences.
- Simplicity. Non-relational databases often only require a few lines of code. Developers don’t have to focus on the relationships between data in the tables. All the information is in one document with links to information that might be needed. This makes distributed data much easier.
- Less maintenance. Relational databases require more ongoing database administration than non-relational databases. This is because non-relational databases can automatically partition and duplicate information across nodes.
- Scalability. Non-relational databases can be scaled horizontally and vertically and can be modified to meet the needs of a business. This means they can be widely distributed. There is less structuring than in relational databases, which means every item is self-contained and independent. Each object is kept on a separate server and doesn’t have to be linked or joined.
What should I know before choosing between relational databases and non-relational databases?
What type of data will you be working with?
Do you foresee multiple data types and sources? If so, then non-relational may be better. In addition, non-relational databases can store unlimited sets of data and can quickly change with various requirements.
What kind of resources can you dedicate to your database?
With a smaller engineering team, a relational database makes more sense because it takes less time to manage. In addition, chances are good that your engineers already know the SQL language since it’s so popular. Non-relational databases demand more programming know-how and that could mean some members will need the time to learn different query languages, although now some non-relational vendors have SQL compatibility. (Or you’ll have to hire someone who already has such skills, which could also be costly and time-consuming.)
Do you need real-time data?
Real-time data is often touted as a way to gain a competitive edge. That’s true, but some organizations don’t need real-time data. If you’re looking at historical data, for example, you don’t need real-time data. It’s important to consider your organization’s strategy and how best to meet the organization’s needs and goals when looking at relational or non-relational datasets.
Does it need to scale?
If your data is expected to grow – such as a greater focus on customer data – then you need a database that can grow with it. If this is the case, non-relational systems excel at growing over time.
Can it meet reliability and performance demands?
Consider things like the requirements for query-response performance or what could happen with unplanned downtime. Does the vendor have a service-level agreement (SLA) that makes sense for your organization?
FAQs about relational databases vs non-relational databases
When should I use a relational database?
You should use a relational database when you need data to be easily structured into categories and it’s consistent in input, meaning, and easily navigated. Another time to use a relational database is when relationships are easily defined between the data points.
When should I use a non-relational database?
A non-relational database is the right option if your data isn’t confined to a structured group. It can also be the best fit if you need to perform functions that provide greater flexibility or you need to make more variant inputs. A non-relational database can also give you faster access to data, which may be important if your business needs speed and simple accessibility over reliability and consistency (typically).
What is an example of a relational database?
An example of a relational database is Microsoft SQL Server, Oracle Database, My SQL, IMB Db2, SQLite, and PostgreSQL.
What is an example of a non-relational database?
A non-relational database does not have tables. It uses a storage model that is geared for specific requirements of the type of stored data. Some of the more well-known non-relational databases include MongoDB, Redis, Apache Cassandra, Couchbase, and Apache HBase.
Find out how Aerospike’s non-relational database solutions can help you meet your business goals.