What are the benefits and drawbacks of using a relational database?

1 Benefit: Data integrity

One of the main advantages of relational databases is that they ensure data integrity, meaning that the data is consistent, accurate, and reliable. Relational databases enforce rules and constraints that prevent data anomalies, such as duplication, inconsistency, or corruption. For example, you can define primary keys, foreign keys, unique constraints, and referential integrity to ensure that each record has a unique identifier, that related records are linked correctly, and that no invalid or orphaned data is inserted or deleted.

2 Benefit: Data security

Another benefit of relational databases is that they provide data security, meaning that the data is protected from unauthorized access or modification. Relational databases allow you to define user roles, permissions, and encryption to control who can access, view, edit, or delete data. For example, you can grant or revoke privileges to specific tables, columns, or rows, or you can encrypt sensitive data to prevent data breaches or leaks.

3 Benefit: Data flexibility

A third benefit of relational databases is that they offer data flexibility, meaning that the data can be easily queried, manipulated, and analyzed. Relational databases use SQL, a powerful and standardized language that can perform complex operations on data, such as filtering, sorting, grouping, joining, aggregating, and calculating. SQL also supports various functions, operators, and clauses that can handle different data types, formats, and scenarios. For example, you can use SQL to perform text, numeric, date, logical, or conditional operations on data, or you can use subqueries, joins, unions, or views to combine data from multiple tables.

4 Drawback: Data scalability

One of the main drawbacks of relational databases is that they have limited data scalability, meaning that they cannot handle large volumes or high velocities of data efficiently. Relational databases are designed to store structured data in fixed schemas, which makes them rigid and inflexible when the data grows or changes. To scale a relational database, you either have to upgrade the hardware or partition the data across multiple servers, which can be costly, complex, and risky. For example, you might face performance issues, data fragmentation, or synchronization problems when scaling a relational database.

5 Drawback: Data complexity

Another drawback of relational databases is that they introduce data complexity, meaning that they require more design, maintenance, and administration than other types of databases. Relational databases depend on a careful and logical design of the tables, columns, keys, constraints, and relationships to ensure data integrity and efficiency. However, this also means that any changes to the schema or the data can have significant impacts on the database functionality and performance. For example, you might have to modify multiple tables, indexes, views, or triggers when changing the schema or the data.

6 Drawback: Data diversity

A third drawback of relational databases is that they lack data diversity, meaning that they cannot store or process unstructured or semi-structured data effectively. Relational databases are optimized for storing data in rows and columns, which is suitable for numeric or textual data, but not for other forms of data, such as images, videos, audio, documents, or JSON. To store or query such data in a relational database, you either have to convert it into a binary format or use special extensions or functions, which can be cumbersome, inefficient, or incompatible. For example, you might have to use a large object (LOB) type or a JSON function to store or query unstructured or semi-structured data in a relational database.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?