Database design plays a crucial role in the efficiency and effectiveness of an online transaction processing (OLTP) system. Mistakes in database design can lead to long-term issues that can jeopardize the performance and stability of the system. In fields such as healthcare, finance, and government, where large amounts of data are processed concurrently, a poorly designed database can result in slow queries, website crashes, and data integrity problems.

One common mistake in database design is the lack of foresight about the expansion of data. Developers often fail to anticipate the extent and pace of data growth, leading to scalability issues in the future. It is important to plan for the current and future needs of the organization and consider the potential expansion of data.

Another mistake is the failure to align the database model with real-world relationships. For example, in a database model, it is important to reflect the fact that people can have multiple addresses, employers, or even names throughout their life. By accurately representing these relationships, the database can better handle the complexities of real-world data.

There are two main types of databases: centralized and distributed. Each has its own advantages and the choice depends on the needs of the organization. Centralized databases are often used by large businesses and governments to consolidate data and reduce redundancy. On the other hand, distributed databases provide redundancy and fault tolerance, allowing users to access data even if one database fails.

When designing a database, there are several best practices that should be followed:

1. Strong, Unique Primary Keys

It is important to generate unique primary keys for each entry in the database. This helps to ensure data integrity and reduce redundancy. Whenever possible, use natural keys such as social security numbers (SSN) as primary keys. If natural keys are not available, generate surrogate keys based on a combination of columns that can uniquely identify each entry.

2. Use of Parent and Child Tables

Divide large tables into parent and child tables connected through primary key/foreign key relationships. This helps with data normalization, allows faster updates, and reduces data redundancy. For example, in a table storing information on government benefit recipients, a child table can be used to list the doctors associated with each recipient.

3. Indexing

Indexes provide a reference to where data is stored in a table, speeding up queries. It is important to keep the number of indexes relatively low to maintain efficiency. By default, an index is created on primary key columns. Additionally, functional-based indexing can be used to improve performance by carrying out functions inside SQL queries.

4. Partitioning of Tables

Large data tables can be partitioned into smaller physical parts based on criteria such as date or numerical values. This allows for quicker searching and retrieval of data. For example, a table of government benefit cases can be partitioned by dates, such as by year or month.

By following these best practices and planning for the current and future needs of the organization, a well-designed SQL Server database can provide a powerful and efficient solution for data management. It is important for organizations to prioritize the importance of good database design and provide developers with the necessary information to create a high-functioning database.

About the Author: Vikram Bachu is a software engineer specializing in ETL development. With 10 years of experience in the industry, he is highly skilled in Informatica, IICS, UNIX, and multiple databases. For more information, contact vikrambachu9@gmail.com.