One of the most significant decisions you’ll need to make when building a strong and scalable backend of your application will be selecting the best database technology for storing data. In all reality, your database is the backbone of an application: most data storage and processing, which feed functionality, user experience, and performance-are residing there. Currently, with as many different database technologies that are now available, making a choice about which to use is far from an easy decision at all. Be you building a new system, moving an existing one to a new environment or scaling an existing application; it is in your best interest to understand the kinds of databases and what strengths they hold so that this will guide your decision-making process. This article will cover the main types of databases and their characteristic features, one of the key aspects in backend development.
Types of Databases
There are two big categories of databases: relational databases and non-relational databases, also known as NoSQL. The primary difference between both of the categories is that each category serves different needs depending on the type of data or the nature and scale of the application.
1. Relational Databases
Relational databases have been the bedrock of data management nearly from its very beginnings around three decades ago. Data is managed and queries against it that resides in tables with rows and columns are based on structured query language (SQL) and, in this manner, each table defines a different entity or kind of information, and relationships among various data entities are maintained using keys, making them extremely effective for complex queries as well as transactional systems.
Well-known relational databases include:
- MySQL: This is one of the most common relational databases; actually, MySQL can be termed as one of the fastest, most reliable databases amongst others, and pretty easy to work with. Therefore, it becomes pretty popular for using in web applications as well as in content management systems.
- PostgreSQL: Such a strong database is PostgreSQL. As an open-source product, PostgreSQL provides implementations like custom data types, full-text search, and support for geographic objects; thus it turns out to be great for complex and data-intensive applications.
- Microsoft SQL Server: Usually the preferred choice of a database in the enterprise environment whenever integration with other technologies developed by Microsoft is needed. The database does offer scaling, security, and complex queries.
- Oracle Database: Enterprise leader in databases, high availability, security, and performance: good for high-end applications where data integrity is crucial
When to Use a Relational Database:
Other types of systems will shine on data integrity, consistency, and structure matters when crucial. When you need to run complex queries, these same systems will prove useful. For instance, accounting systems, user management, or inventory tracking is all an application scenario where relational databases are primarily used. Your applications adhere to the ACID rules as well and, hence, handle transactions.
2. NoSQL (Non-Relational Databases)
NoSQL databases are not simply relational databases; they are meant for the storage and management of huge sets of unstructured or semi-structured data. Instead of depending on tables and SQL for the storage and retrieval of data as in relational databases, NoSQL databases are much more flexible and scalable for storing data in formats such as key-value pairs, documents, graphs, or wide-column stores.
Popular NoSQL Databases:
- MongoDB: Probably the most-famous NoSQL document-oriented database, MongoDB stores data in JSON-like documents. This will likely be of good help with working with hierarchical data structures. It is a good and flexible choice for developers dealing with variable data and fast development cycles.
The following are some of the most notable NoSQL systems:
- Cassandra: This is a wide-column store that scales up pretty well with inherent fault tolerance features. Its real strengths are in distributed systems where high availability is really very important; thus, it is a great pick for big data applications.
- Redis: It is a key-value store running in-memory. Redis is faster and often used as a cache layer for systems behind applications. Data types are strings, hashes, and lists.
- Neo4j: It is a graph database that stores data in nodes and relationships, with complex, interconnected, or huge amounts of data, such as social networks or recommendation engines
When to Use NoSQL Database
The NoSQL databases do well in data with dynamic or schema-less structure. Content management systems, social media, and IoT applications are good examples. They are excellent for applications that demand huge volumes of data, lots of reads and writes, or have scaling needs that can be met with horizontal scaling. And NoSQL databases will do best for work with unstructured data like log files, or when your application needs to operate on very large collections of highly heterogeneous data without being bothered by strict schemas.
Key Considerations When Selecting the Proper Database
I have used a number of factors to help make the right database technology choices when there is some specific need that should be addressed in the application. Below are some of the basic considerations to guide your decision.
1. Data Structure and Relationships
The database to be chosen for storing data will be chosen solely on the nature of data you plan to store. A relational database will be ideal in case your data is structured and well-arranged in related tables. In case your data is unstructured or semi-structured, then NoSQL databases will be suitable because they feature flexible schema design.
2. Scalability
All distributed databases require the addition of more servers in order to scale horizontally, but no distributed database in itself will compromise this. Relational databases can be horizontal or vertical in scaling and tend to become complicated with too much data or extremely high throughput. In a NoSQL database, it is built to scale horizontally, thus supporting large volumes of data.
3. Performance and Speed
If your application is one where reads are very frequent and writes are also high speed, then watch for performance in databases. Relational databases deliver consistency and allow you to support complex queries, but NoSQL databases generally outperform relational databases for specific workloads-particularly big data and where full transactional integrity isn’t a factor.
4. Consistency vs. Availability
Distributed systems entails a trade-off between consistency and availability. Relational databases implement the ACID properties exactly to ensure data consistency. They perform pretty poorly at availability, especially in highly distributed architectures. Most NoSQL database variants, such as Cassandra, are based on the CAP theorem: they assume availability and partition tolerance and then yield when it comes to consistency.
5. Use Case and Domain
It will mainly depend on the type of your application. For example, when you have long transactions and the nature of transactions depends on it, it will be better suited in the relational databases. However, most probably, NoSQL solutions like Cassandra or Neo4j will fit other domains, for example, real-time analytics, social media, or recommendation systems.
6. Ecosystem and Support
Another great aspect is the ecosystem of the technology itself. Some databases have impressively wonderful community support, documentation, and third-party tools that do quite a bit to facilitate development. If you are in an enterprise environment, look for a database that does a great job of integrating into your existing tech stack and gives you features like backups, monitoring, and security.
Conclusion:
Ultimately, the right database for your backend will depend on what your project actually needs. Here are some guiding principles to help make a decision:
- Choose relational database: If your data is structured and relationships are important and the application would need higher consistency and querying.
Note that this is further divided into two sub-types. If your data is semi-structured or unstructured, your system scales horizontally, or you have very high throughput and flexibility requirements, use a NoSQL database. Hybrid approach: You might be in situations where you use relational and NoSQL databases together. This is more commonly known as polyglot persistence. Today you see many applications being built to exploit the strengths of these different technologies available at your disposal.
The most crucial decision while building the backend is determining the right database technology; that is going to be very impactful on the scalability, performance, and maintainability of your application. Relational databases are by far the best suited to applications that work with structured data and extensive queries, but NoSQL databases shine in situations with unstructured or constantly evolving data, where flexibility and scalability are the greatest values. Setting up your application on an informed decision-that then considers the type of data structure, whether it is scalable or not, the consistency needs, and possible use cases-adds to setting all the necessary bricks for success. As time progresses, keeping in touch with advancements in the field of database technologies will help optimize your backend for today and tomorrow’s requirements.