Relational databases and non-relational databases primarily differ in the types of data they store and how that data is organized.
Relational is highly structured, while non-relational is unstructured. This key design difference makes them well-suited to very different business functions, applications, and programs.
What is a relational database?
A relational database (also known as a structured database) is designed to store structured data types in tables with rows and columns, similar to a spreadsheet. Because structured data fits into defined formats, categories, and types, it can be easily sorted into neat, organized tables. Data in a relational database can also be easily retrieved, searched (queried), and manipulated.
Common examples of relational database systems (RDBMS) include:
- MySQL, an open-source database widely used for web applications and data storage.
- PostgreSQL, an advanced open-source relational database known for its robustness and support for complex queries and data types.
- MariaDB, a fork of MySQL that offers enhanced performance, stability, and features.
- IBM Db2, known for its enterprise-level features and support for large-scale data management.
- Microsoft SQL Server, popular in enterprise environments for managing and querying structured data.
- Oracle Database, a powerful commercial RDBMS known for its scalability, security features, and widespread use in large enterprises.
Take an airline's flight data, for example. Every flight's entry includes structured data such as the flight number, departure and arrival times, origin and destination airports, stops, the number of seats, airplane type, and so on. This data can be easily filtered, searched, manipulated, and cross-referenced with other relational databases on flight booking websites, airport arrival monitors, customer service centers, and by the FAA.
Editor's Note:
This guest blog post was written by the staff at Pure Storage, an US-based publicly traded tech company dedicated to enterprise all-flash data storage solutions. Pure Storage keeps a very active blog, this is one of their "Purely Educational" posts that we are reprinting here with their permission.
How relational databases store and retrieve data
The structured nature of the data entries means relationships can be established between entries in the same table and other tables with primary keys and foreign keys, respectively. This makes it possible to interlink related data sources and create more complex data structures and reports.
Relational databases store and retrieve data with:
- Rows, which make up entries or records. Each row is a unique record (e.g., a flight, in the example above).
- Columns, which are fields of the entries. Each column is a unique attribute (e.g., number of stops, in the example above).
- Primary keys, which establish each unique row.
- Foreign keys, which establish relationships between related tables with related data.
- Structured query language (SQL)-a standardized language used for querying, editing, or retrieving data in relational databases.
What are the features of a relational database?
The features of relational databases include:
- Rigid schemas. The structured tables of relational databases are by design, but not always, a good fit for workloads and data that are bound to change and evolve over time.
- Concurrent transactions or serial transactions, to avoid data corruption when multiple users are accessing a database.
All of these features add up to a highly scalable, secure, and high-performance way to concurrently access structured data.
What data storage is best for relational databases?
Relational databases have long been the territory of traditional disk-based storage technology, but that is changing as more data centers look to improve cost, efficiency, and power consumption. Flash storage can offer an affordable and highly consistent storage solution for relational databases as they grow and support more cloud-based modern applications' persistent storage needs.
SSDs, which are flash-based, are beneficial for low latency and performance for relational databases. Network attached storage (NAS) can offer connected storage and shared access to large amounts of data stored in relational databases across multiple servers. Cloud-based storage is also an excellent option for getting the scalability and reliability many enterprise applications leveraging relational databases need.
What technologies use relational databases?
Technologies that use relational databases include many large-scale, enterprise applications but can also include the structured workloads of modern applications. You'll find relational databases behind any large amounts of structured data, such as user databases, even on platforms like social networks that rely on NoSQL databases.
Technologies that use relational databases can include:
- E-commerce platforms supporting large inventories / structured product data (e.g., SKUs)
- Accounting and banking software
- ERP (enterprise resource planning) and CRM (customer relationship management) platforms and business intelligence platforms
- Supply chain management software
- Electronic health record (EHR) systems
- Government and municipal applications and records
What are relational databases best at?
- Efficiency. A highly structured and predictable schema means it's fast and efficient to read, write, and query data.
- Consistency. Consistency and accuracy are enforced in relational databases by design. This keeps entries and records consistent and builds in integrity to adhere to schema rules and relationships. (This also reduces the likelihood of duplication.)
- Vertical scale
- ACID compliance. Relational databases typically follow the ACID (atomicity, consistency, isolation, durability) properties, ensuring data transactions are processed reliably and securely.
What is a non-relational database?
A non-relational database is more commonly referred to as a NoSQL database or an unstructured database. NoSQL means "not only SQL," which is the standard query language for relational databases. If relational databases are designed for structured data, NoSQL databases are designed for all the rest-semi-structured and unstructured, which do not fit neatly into tables.
NoSQL databases follow a more flexible data model, which is ideal for storing data that changes frequently or for applications that handle diverse types of data.
Common examples of NoSQL databases, with different models and specialities, include:
- MongoDB, a document-oriented database that stores data in flexible, JSON-like documents.
- Cassandra, a distributed database system designed for handling large amounts of data across many servers, based on a wide-column store model.
- Redis, an in-memory key-value store known for its speed and use in caching and real-time analytics.
- Couchbase, a distributed NoSQL database that combines document storage, key-value, and full-text search.
- HBase, a distributed, scalable big data store, modeled after Google's Bigtable, and built on top of Hadoop.
- DynamoDB, a fully managed NoSQL database service provided by AWS, known for its low-latency key-value and document data storage.
- Neo4j, a graph database designed to represent relationships between data using graph structures.
Considering the airline database example we mentioned before, a NoSQL database would, in this case, store semi-structured and unstructured data related to flights such as readings from airplane sensors and gauges, in-flight recordings and comms, GPS and mapping, regional conditions during the flight, and so on.
It's important to note the variability of data models between NoSQL databases. When choosing from open source databases, be sure to take into consideration the developer support, your needs for scalability, and what data model is best for your workload.
Here's where we start to get into more dynamic, modern-day data applications and use cases for unstructured data and its potential.
How do non-relational databases store and retrieve data?
NoSQL databases organize this data with alternative models to SQL databases that aren't as rigid. This allows for a more flexible approach to grouping, storing, and searching data of different types such as photos, audio, or sensor data. These models include:
- Key-value pair databases, in which data is stored and retrieved based on keys
- Graph databases, with the primary focus on relationships between data and queries
- Document store databases, using JSON or XML formats and unique document structures
- Columnar databases, which use columns instead of rows for discrete grouping and analysis
What are the features of a non-relational database?
NoSQL features are all about scale, variety, and flexibility-mirroring those of modern applications and data:
- Highly flexible schemas. While SQL databases have a rigid structure by design, the schema of a NoSQL database can evolve over time. This is a benefit when storing data that's unstructured and likely to evolve over time, thus not always able to fit into the same, rigid schema. Here's where eventual consistency helps, propagating changes made to a database across nodes over time.
- Built-in replication. NoSQL's ability to replicate and distribute data across the nodes mentioned above gives these databases high availability but also failover and fault tolerance in the event of a failure or outage.
- Sharding, which improves scalability and load balancing. This technique allows data to be spread vertically across multiple nodes and servers, distributing workloads and dividing data sets up so processing can be done on a smaller scale in parallel. Also, this improves fault tolerance.
What data storage is best for non-relational databases?
Compared with SQL databases, NoSQL databases will have very different requirements from data storage. NoSQL databases need the capabilities of SSD flash storage, and more specifically, object-based storage to support large-scale unstructured data volumes.
Object storage like Pure Storage FlashBlade is well-suited for the storage and consolidation of large unstructured files such as images. In-memory storage, while it has certain limitations, is another option for real-time applications and caching use cases.
Storage architectures must be designed to support the demands of large NoSQL databases with the proper scalability and performance, all while reducing the overall data center footprint.
Using non-relational databases
As mentioned above, NoSQL databases are able to meet the unique demands of modern data and applications. There are complexities and drawbacks, but many have been addressed with modern unstructured data storage technologies that deliver highly scalable, affordable, and performant solutions that offset these issues.
What do non-relational databases do best?
- Unstructured data management. Relational databases were not built for unstructured and semi-structured data. Storing this data, and storing it in a way that makes it accessible and easily leveraged, is the primary thing NoSQL databases do best.
- Horizontal scale. As traffic grows and data volumes expand, NoSQL databases can flex to meet demands by adding nodes to existing clusters.
- Complex analytics and real-time analysis of big data. Storing large volumes of data is one thing; being able to efficiently process and analyze it is another. This is where NoSQL databases excel.
- Distributed and cloud-based applications
Who uses NoSQL?
NoSQL databases are the databases of modern applications and data-heavy use cases such as:
- Large-scale web applications, with caching as a top use case
- Big data analytics
- Fraud detection
- Recommendation engines
- Time-series databases
- AI and machine learning, with flexible data modeling requirements
- The internet of things (IoT) and sensor device networks
- Real-time data analytics and processing
- Social media networks, which rely on unstructured data and massively parallel requests
What these apps and technology have in common are their demands: massive scalability, high performance, flexibility, and capacity for highly varied data types.
Key differences between relational and non-relational databases
Relational database pros include integrity, consistency, and reliability. They're great for highly structured data, which will always be a critical workload.
Non-relational databases can tackle the demands of unstructured data, with advantages for distributed environments and modern workloads that require deeper, more complex analysis.
But aside from these structured vs. unstructured data advantages, there are other differences to consider:
- Avoiding data duplication. Relational databases promote data consistency by avoiding data duplication and adhering to normalization principles. Normalization eliminates redundant data and minimizes data anomalies, ensuring efficient storage and maintenance of data.
- ACID compliance. Relational databases may be better suited to ACID compliance (atomicity, consistency, isolation, durability). ACID transactions guarantee that database operations are performed as a single, indivisible unit and that the database remains in a consistent state even in the presence of failures or concurrent access.
- Concurrency. By design, relational databases can help to avoid issues with consistency or congestion when large amounts of requests are made from multiple users. Handling these concurrent queries and transactions makes relational databases an advantage in scenarios where large amounts of structured data need to be accessed at once without compromising the integrity of the data or the application.