7 Types of Databases That Every Backend Developer Should Know
Have you always wondered which database you should choose for your next project? Should you pick relational, non-relational, or something else? Each database type works best for its specific use case. You should definitely consider it before making the final decision. This article will give you some insights into different types of databases, their pros, cons, and use cases.
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1. Relational databases
Let’s start with the most common ones – relational databases. They were invented over 50 years ago, and they’re still one of the most popular databases nowadays. Relational databases require us to have a predefined schema in order to organize the data into tables, columns, and rows. We use the SQL language to perform operations such as creating, updating, deleting, and reading.
- Applications with well-structured and consistent data
- Applications with data containing many relationships
- Social networking websites
- Stock trading apps
- ACID transactions
- Data consistency
- Hard to scale
- Limited amount of complex data types (e.g., drawings)
- Changing the predefined schema can be very time-consuming
2. Document-oriented databases
Moving on to the next type – document-oriented databases. They store entries in the form of documents (e.g., JSON, BSON, XML). They don’t require a predefined schema, and the data doesn’t have to be well-structured. Document-oriented databases are commonly used in many modern applications, and a lot of people consider them easier to work with than relational databases.
- Applications with unstructured and semi-structured data
- Mobile applications
- Content Management Systems
- Easy to scale
- High performance
- No ACID transactions (in most cases)
- Lack of a query language (e.g., SQL)
- Not that mature yet
3. Key-value databases
Key-value databases are used to store data in a way that each key is unique and holds a specific value. The whole structure of the data is similar to a hash table or dictionary. Thanks to storing data inside machine memory, key-value databases are incredibly fast. On the other hand, such architecture limits the amount of data that you can keep.
- Session storage
- URL Shorteners
- Extremely fast
- Flexible to work with
- Limited amount of data that you can store
- Bad for complex queries
- Scaling can be costly
4. Wide-column databases
The next type on our list – wide-column databases. They are designed to store a massive amount of data in a scalable way. Wide-column databases don’t follow the relational model, and they are not optimized for joins operations. What’s interesting, they can are called two-dimensional key-value stores.
- Applications with frequent writes and occasional reads.
- Time series data
- Big, large scale applications
- Superfast writes
- Easy to manage large data sets
- Slow reads, updates, and deletes
- Effective indexing can be hard to design
5. Search-engine databases
Search-engine databases work very similarly to document-oriented databases. The main difference is that a search-engine database analyzes all the text in the document and creates an index of searchable terms. They are very popular when it comes to filtering and finding data in large data sets. Moreover, search-engine databases store unstructured data without having a predefined schema.
- Search engines
- Typeahead search box
- Very scalable
- Extremely fast searching
- Flexible schema
- Not efficient when it comes to reading and writing data outside searching
- Not very secure
- No transactions support
6. Graph databases
Graph databases are NoSQL databases that use graphs, nodes, and edges to represent the data. All the information is stored inside nodes, and it uses edges to make connections between them. With such architecture, it’s very easy and straightforward to visualize all the data inside our storage.
- Social networking applications
- Fraud detection systems
- Geological routing and visualization
- Operation speed is based on relationships and not on the amount of data
- Allow a compact and clear representation of data
- Have flexible and agile structures
- Not suitable for simple use cases
- Bad performance of aggregate queries
- Difficult to scale
7. Multi-model databases
Multi-model databases are about implementing various data storage technologies to handle different use cases. Let’s take a look at the most popular one – FaunaDB. If you’re a frontend developer or you just don’t want to think about modeling, data replication, sharding, schemas, etc. – this database might be the right choice for you. With FaunaDB, you describe how you want to get the data using GraphQL or FQL. Under the hood, FaunaDB takes advantage of other database types and picks the best choice for your specific use case.
- Real-time consumer applications
- Financial services
- Extremely fast
- Takes advantage of other types of databases
- Offers ACID transactions
- Limited support
- Relatively young technology
7 Types of Databases – Conclusion
There are many interesting types of databases that you should consider for your next project. Each of them has its own pros and cons, as well, use cases. You should think about your specific project and decide which type fits best into your case. Sometimes, you’ll need to use more than one database type. You can also take a look at how big-tech companies approach this problem. How can they handle such a significant amount of traffic? If you want to learn more about it, here are great articles describing their architecture.
Remember that our industry changes very rapidly. Some technologies are being replaced by new ones every week. Therefore, it’s crucial for us to never stop learning and improving ourselves. So keep this in mind and create some amazing projects that will move you to the next level!
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