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 Are Relational Databases?
Relational databases are designed to organize and store structured data efficiently. At their core, these systems use a data model based on tables, where information is arranged in rows and columns. Each table, also known as a relation, is connected to others through common fields, enabling relationships between datasets. This structure ensures high levels of data integrity and simplifies complex data handling.
A defining characteristic of relational databases is their use of structured query language (SQL). SQL allows users to create, modify, and query data with precision. Whether performing a basic data retrieval or executing a complex query spanning multiple tables, SQL provides functionality to meet specific needs. The predefined schema in relational databases enforces consistency, making them reliable for applications where structure are paramount.
Example:
Imagine a company managing customer information and sales orders. A relational database might include:
Customer Table
: Contains customer IDs, names, and contact details.
Orders Table
: Includes order IDs, customer IDs, product details, and order dates.
Product Table
: Stores product IDs, names, and prices.
By linking these tables through shared fields like customer IDs or product IDs, the company can easily generate insights, such as a report showing all orders placed by a specific customer. This interconnected structure not only simplifies data retrieval but also ensures data accuracy across the system.
How do relational databases store data?
Relational databases store data using a structured approach that revolves around tables, rows, and columns. Each table, defined by a predefined schema, represents a specific entity, such as customers or orders, and contains rows for individual records. This data structure ensures that data is stored in a tabular format, where each row corresponds to a unique record and each column to a specific data type. For example, a customer table might include columns for customer ID, name, and email address, with each row representing a different customer.
The strength of relational databases lies in their structured data format, which allows for complex queries using structured query language (SQL). SQL facilitates complex querying, enabling users to retrieve and manipulate data across multiple tables with precision and efficiency. A typical SQL query might involve joining tables to get a comprehensive view of related data, such as fetching customer details alongside their purchase history.
One key feature of relational databases is their emphasis on data integrity and referential integrity. This is achieved through the use of constraints and relationships between tables, ensuring that data remains consistent and accurate. For instance, a foreign key constraint might prevent the deletion of a customer record if there are related orders in another table.
The data model of a relational database is characterized by its rigid structure, which requires careful planning and design.
What are non-relational databases
Non-relational databases differ fundamentally from the traditional relational database approach. They do not rely on a predefined schema, enabling them to handle semi-structured data and non relational data with a flexible schema. This adaptability makes them ideal for various use cases where diverse data types and structures are involved. Unlike relational databases that organize structured data into multiple tables with referential integrity, non-relational databases utilize various models like document databases, key value pairs, column-family stores, and graph databases.
Document-based databases
Document databases store data as documents, often in formats like JSON or XML. This type of database treats data as a single entity, allowing for semi-structured data with nested fields.
Key-value stores
In key-value stores, data is stored as simple key value pairs. This model is highly efficient for scenarios where retrieval speed is paramount. Key-value stores excel in applications like caching and session management, where rapid access to data is crucial.
Column-family stores
Column-family stores organize data by columns rather than rows, optimizing for read and write speeds in large datasets. This architecture suits big data analytics, where quick access to vast amounts of data is required. These stores handle complex queries efficiently by focusing on specific columns.
Graph databases
Graph databases prioritize relationships between data points, representing data as nodes and edges. This structure is ideal for applications involving complex interconnections, such as social networks and recommendation engines. Graph databases enable intuitive traversals and uncovering hidden patterns in data.
How do non-relational databases store data?
Non-relational databases store data without a strict predefined schema, allowing for greater flexibility in data storage. This capability supports the storage of semi structured data and non relational data in ways that can evolve with application needs. Data is stored in formats like documents, key-value pairs, columns, or nodes and edges, depending on the database type.
With their flexible schema, non-relational databases accommodate evolving data requirements and offer scalability across multiple servers, supporting various data management challenges.
When to use relational vs. non-relational databases
The decision between a relational database vs non relational database should be guided by the specific requirements of your project, including the nature of the data and the expected workload. In scenarios where structured data and data integrity are the most important, a traditional relational database can get the job done. These databases excel in handling complex queries that involve multiple tables and ensure referential integrity through structured query language (SQL).
Conversely, if your project is dealing with non relational data, such as semi structured data or big data, which necessitates a flexible data model, non relational databases come into play. These databases, including document databases and graph databases, provide flexible schema capabilities, enabling them to handle key value pairs and complex querying more efficiently over multiple servers. Their adaptability makes them ideal for applications where data is distributed across various locations.
For projects emphasizing data governance and data consistency, especially in environments with a strong data management focus, relational data systems are often aligned with these needs. However, when rapid development and deployment are critical, and the data model needs to adapt swiftly to changing requirements, non relational databases may offer the needed agility.
Use cases are pivotal in guiding the choice of database. For instance, a document database might be used for content management systems requiring flexibility in data structure, while a graph database might be utilized for social networks where relationships between entities are complex.
Ultimately, understanding the specific data architecture and the types of data stores you are working with will inform the most suitable choice between these database types. Whether you require the robust transaction support of a relational database or the scalability and flexibility of a non relational database, aligning your choice with your project's goals will ensure optimal performance and efficiency.
