Denormalized advantages review

We have covered the importance of normalizing a database. It reduces data redundancy, decreases the opportunity for update/delete anomolies and improves data integrity generally.

In short, it will result in a reliable database.

But surely a denormalized database must have some advantages...

Advantage	Description
Queries are simpler	Without joins to consider, queries are simpler to design
Queries are faster	Queries can be executed on one table, rather than navigating joins, making query-execution faster.
Less processing	Because joins are not needed (or reduced), less processing power is required

Of course, in a denormalized database, there is a risk of:

1. data duplication
2. update/delete anomolies
3. generally poor data integrity

However, in scenarios where a fast read is required, a denormalized database can out-perform a normalized database.

So when choose to normalize a database or leave it denormalized, the benefits will have to be weighed up against the drawbacks.

Limitations of Databases

Let's consider some limitations of sql databases in general:

Limitation	Explanation
"Big data" scalability issues	Traditional databases can struggle when the volume, speed, or variety of data becomes extremely large. Storing, processing and querying huge datasets can require significant computing resources and specialised systems.
Design complexity	Designing a database can become complicated, particularly when there are many tables, fields, constraints and relationships. Poor design can lead to redundancy, inconsistency or inefficient queries.
Hierarchical data handling	Traditional relational databases are designed around tables and relationships, which can make naturally hierarchical data (e.g. a family tree or organisational structure) less straightforward to represent and query.
Rigid Schema	A relational database normally has a predefined structure (schema). Changing the structure of a table can be difficult when large amounts of existing data and applications depend on it.
Object-relational impedance mismatch	Programming languages commonly represent information as objects, while relational databases represent it as tables and rows. Converting between these two representations can add complexity to software development.
Unstructured data handling	Relational databases are well suited to structured data, but data such as images, videos, audio, social-media posts or free-form text may not fit naturally into rows and columns.