Designing a Database Schema

If you want to build a solid foundation in data management, then the first step is designing a database schema. If you are not well-versed in designing an effective database schema , you might end up creating databases that are:

  • Poorly defined
  • Extremely difficult to administer and maintain
  • Heavy consumers of memory.

The main aim of this writing is to provide you with a detailed discussion of effective data schemas. This content will also be based on PostgreSQL.

The following are some of the considerations that you should make when designing a schema:

  • The design of any schema is logically determined by the business requirements. The design of a database is vital because it plays a key role in how the database will be developed. The needs of a business can translate into essential information that a programmer can utilize when physically designing the database. Also, a set of rules definitions, sometimes called constraints, is part of this process. It is these constraints that define how various tables relate to each other
  • Definition of the physical layout of the database. The physical layout will define the location of logs, data files, and other related objects. Defining all the attributes of a schema adds logic and clarity to its environment.
  • Definition of the schema security. This will be based on the user of the schema. Security determines the tables a given user has access to.
  • The use and definition of the appropriate naming conventions. It is essential to understand your data and the attributes surrounding each element. As you are building out your scope, you will need to consider each element of data. For this reason, you should avoid using reserved words, collations or data types that will inhibit query performance and development of the schema. This step is critical, regardless of whether you are building the database based on planning for additional data or on existing data.

Usage patterns

A database is normally used in two distinct ways:

  • Online Transaction Processing (OLTP)

This type of database has smaller transactions. A good example is a banking system where constant modifications happen throughout the day. Some of these transactions include processing of debit cards, check activity updating account balance, managing the account register security logging activity, etc. The OLTP database strives to organize data to avoid duplication. Limiting fields from repeating data guarantees purity.

  • Decision Support Systems (DSS)

A DSS is mostly used for analytics, data warehouses, and reports. They are generally read-only. Their main job is to aggregate lots of data and generate summary reports. These reports include statistics and calculations which are used by stakeholders to make strategic decisions. Data in DSS is updated irregularly. They can be scheduled daily, weekly and sometimes hourly or any other schedule. The main aim of these databases is to ensure that huge amounts of data can be retrieved quickly. Also, the system is structured in such a way that formulas and calculations are performed before the user requests it.

It is quite difficult to find a database that is strictly DSS or OLTP. Most databases blend the usage of the two but will favor one type or the other.

Naming conventions

Business entities and developers have their preferred style of naming. They can either decide to adhere to an ISO standard (ISO -11179, etc.) or not. However, it is vital to be consistent in how you name fields.

Here are a few helpful tips that can help you with naming conventions:

  • Do not just use the ID as the Primary Key (PK) of each table. This can lead to lots of aliasing when joining other tables and several tables returning multiple IDs
  • Use of SQL Server reserved words like User, Date, etc. for table names, column names and elsewhere will give a syntax error.
  • Hyphens, quotes, and spaces are invalid and require square brackets
  • Tables should be named in singular and not plural. For example, you can name a table customer and order instead of customers orders

A developer may choose to normalize or denormalize the data depending on the expected usage patterns. The table below explains this better:

State of the Database Design

Description

Unnormalized

This is when the database has not been broken apart by business entities into separate tables

Denormalized

The database was normalized at first, then later it was carefully modified to violate normalization rules to increase reporting speed and for strategic reasons

Normalized

The database has been broken into several tables so column values are not repeated.

Hopefully, this article can get you going down the right path in SQL. If you need help with your assignment, do not hesitate to avail of our SQL homework help.