No, a database cannot be fully considered normalised if it only removes redundancy but still allows anomalies. Normalisation is not just about eliminating repeated data — its main goal is to create a structure that also prevents insertion, update, and deletion anomalies. A table might have reduced duplication but still suffer from partial or transitive dependencies, which can cause inconsistent updates or restrict how new data is inserted. For example, if a non-key attribute still depends on part of a composite primary key, an update anomaly may occur, even if the data is not duplicated. True normalisation must address the structure of dependencies between attributes. This includes ensuring that all non-key attributes are fully functionally dependent on the primary key and not on other non-key attributes. Therefore, both the removal of unnecessary redundancy and the correction of dependency issues are essential for a database to be genuinely normalised.

While normalisation offers major advantages like data consistency and ease of maintenance, it is not always the most suitable approach in every scenario. In some situations, especially in systems with very high read-to-write ratios, normalisation can lead to performance drawbacks due to the need for complex joins across multiple related tables. For example, in analytical or reporting systems such as data warehouses, denormalisation is sometimes preferred to optimise read performance and simplify queries. In these cases, redundancy is acceptable if it speeds up data retrieval and reduces join operations. Additionally, if a system’s data structure is very simple or unlikely to change, normalisation may add unnecessary complexity. Therefore, while normalisation is ideal for transactional systems where data integrity and update efficiency are crucial, it may be partially avoided or reversed through denormalisation in systems where performance, simplicity, or speed of access are the main priorities.

Normalisation significantly improves the scalability of a database system, particularly in transactional or operational environments where data is frequently updated. By breaking data into smaller, purpose-specific tables, normalisation ensures that each table focuses on a single entity, making the system modular and easier to expand. As new data requirements arise, developers can add new tables and define relationships without having to restructure existing data. Moreover, normalisation simplifies indexing and reduces the size of each table, which helps optimise query performance as the database grows. This modular design also supports the implementation of microservices and distributed systems, where each service interacts with a small, well-defined portion of the database. However, as databases scale to very large sizes or handle massive concurrent queries, performance tuning may involve caching or selective denormalisation. Despite this, the well-structured foundation provided by normalisation helps ensure that scaling up the system remains manageable and less error-prone in the long term.

Skipping normalisation during the initial database design introduces several long-term risks that can severely affect the reliability and efficiency of the system. Firstly, it increases the likelihood of data anomalies, including inconsistent updates and accidental deletions of important information. Redundancy may lead to conflicting versions of the same data, making it difficult to identify which one is accurate. Secondly, maintenance becomes more difficult because the same piece of information may need to be updated in multiple places, raising the chance of human error. Thirdly, query performance may degrade over time as the database becomes bloated with duplicate and unstructured data. Unnormalised tables are harder to index efficiently, leading to slower searches and more complex queries. Lastly, the lack of a clear relational structure can make future enhancements or integration with other systems much harder. Overall, while it may seem quicker initially, skipping normalisation often results in higher costs and effort later.

Normalisation heavily relies on the effective use of primary and foreign keys to establish clear and logical relationships between tables. A primary key uniquely identifies each record in a table and ensures entity integrity, meaning that every row is distinct and consistently referenced. During the normalisation process, especially when decomposing data into multiple tables, primary keys are used to maintain the uniqueness of records for individual entities like students, tutors, or products. When a table is split, a foreign key is introduced to preserve the relationship between the new table and the original data. Foreign keys enforce referential integrity, ensuring that a value in one table corresponds to a valid entry in another. Without these keys, normalised tables could not communicate effectively, and relationships between data entities would break down. Normalisation therefore strengthens the structure of the database by ensuring that primary and foreign keys are appropriately used to link data in a consistent and enforceable way.