Did you know that choosing a database is one of the critical decisions you make while developing an app?

While RDBMS or relational database management systems currently dominate the market, picking between RDBMS and non-RDBMS isn’t easy. Each technology has its pros and cons. Examine them carefully, see how they align with your business requirements, and then make your choice. Read on to know more.

What is a relational database?

You need an RDBMS to interact with data stored in a SQL or Structured Query Language database. Some examples include MySQL, SQLite, and PostgreSQL. You can perform actions with the database, like updating or reading data using SQL. Here, data is neatly arranged into rows and columns or tables that may also reference each other. The name relational is derived from the relationship formed between the tables.

The column of this table defines what information you are storing, while the rows contain the actual data. Each table has a column containing unique values known as the primary key. This column can be shared between all tables bound by a logical relationship. When the primary key of one table is used in another table, it becomes the foreign key for that table. Some advantages associated with RDBMS include:

• ACID compliance: This Atomicity, Consistency, Isolation, and Durability(ACID) standard guarantees database transaction reliability. For example, if a change is implemented and it fails, the whole transaction will automatically fail. However, the relational database will remain unaffected in the state it was before the failed transaction was attempted. This is a critical advantage because incomplete or failed transactions can play havoc with the data present in the database.

• Data accuracy: Using different keys completely mitigates the risk of data duplication. The absence of repeated information increases data accuracy.

• Normalization: RDBMS lowers storage costs by optimizing data storage through normalization. The data arrangement in a relational database helps eliminate data anomalies to a great extent.

• Simplicity: SQL uses English-like syntax, making it easy for non-developers to generate data reports and queries.

However, even relational databases have certain associated disadvantages, including the following:

• Scalability: RDBMS was historically developed to operate on a single machine. For any increase in data size or frequency of access, you have to opt for vertical scaling of the involved hardware components. This makes the process slightly costly.

• Flexibility: The RDBMS schema is slightly rigid. Hence, you have to maintain the databases keeping in mind their constraints. While this makes it easy to interpret data and establish relationships between tables, making structural data changes becomes very complex.

• Performance: This is directly linked to the complexities of the tables. More number of tables and vast amounts of stored data can result in query-processing delays.

Use relational databases for projects where you can predict the structure, frequency of access, and data type. Additionally, if your project prioritizes relationships between entities, you should always choose RDBMS.

Non-relational Databases: A Brief Idea

Also known as NoSQL databases, a non-relational database has been designed keeping cloud technology in mind. They are great for horizontal scaling but lack the structured data approach of columns and rows present in relational databases. Instead, different database types store data in different way, such as:

• Document databases are JSON-like structures that store various data types in documents. These data types include strings, dates, objects, arrays, nested documents, and numbers like integers, floats, and long. This data is generally stored in pairs. Because of the way the data is arranged, document databases are easy to read, understand, and view. They are individual units that are highly scalable and self-healing. They support horizontal scaling, which makes it easy to duplicate the database across multiple servers yet keep them in sync.

• Key-value databases store data in the most basic way. Information is stored in 2 parts - the key and the value. The key is used to retrieve the relevant database information. You can read and write data very fast in a key-value database. However, this database is restrictive since it does not support complex data requirements.

• Graph databases consist of nodes storing data and edges. Relationship attributes of the data are contained in edges. Relationship-based data searches occur quickly since relationships are defined in the edges. Graph databases are also very flexible because adding new nodes and edges is easy. However, they are not very useful when querying for data with no defined relationship. Neither does it have a defined query language, making it challenging to move between graph databases.

• Wide-column databases are very similar to relational databases. They also store data in tables comprising columns and rows, but you do not have to match the names and formatting of the columns. You can store these columns across multiple servers. They reference data by column and rows using multi-dimensional mapping. Wide-column databases are flexible and process queries very fast. They are good at handling unstructured Big Data because of this flexibility. In comparison to relational databases, they are slower in handling transactions.

Non-relational databases have some consistent advantages, making them the best choice when working with flexible data. Since it has been created to sync with the cloud, it supports horizontal scaling. Therefore, choosing a non-relational database makes sense if your project requires working with flexible data likely to change shape, size, and other properties.

In conclusion

So, which database should you choose? The answer depends on your project requirement, your organization’s needs, and the app functionalities you want to achieve. Hence, go through the two types of databases, understand their features and use cases, and then finalize on a decision.