Database — Keep Data and Data Schema Separate

Separating data and data schema is a key notion in database architecture and data management. This separation assures the integrity, flexibility, and maintainability of the data. Database normalization and layers of abstraction can be utilized to achieve the principle of separating data and data schema.

Database — Keep Data and Data Schema Separate

The majority of companies use external tables in OLAP databases to separate data and data structure. In most cases, this is significantly less costly than storing all of the data in the OLAP database. Here’s why it’s essential as well as how it can be implemented:

• Data Integrity: By separating data and data schema, you can guarantee each piece of data is saved in the correct format and complying to the restrictions. It lowers the possibility of data duplication and inconsistency.
• Flexibility: By separating data and data schema, you may adjust the database structure without impacting the existing data. Without losing current data, you may add or delete columns, alter data types, or create new tables.
• Maintainability: When data and schema are separated, the database is easier to administer and upgrade. Changes to the schema do not need changes to the real data, which reduces the chance of mistakes and data corruption.
• Scalability: A well-designed schema can aid in…