What Is Good Database Design?

This is an article from DZone's 2023 Database Systems Trend Report.

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Good database design is essential to ensure data accuracy, consistency, and integrity and that databases are efficient, reliable, and easy to use. The design must address the storing and retrieving of data quickly and easily while handling large volumes of data in a stable way. An experienced database designer can create a robust, scalable, and secure database architecture that meets the needs of modern data systems. 

Architecture and Design

A modern data architecture for microservices and cloud-native applications involves multiple layers, and each one has its own set of components and preferred technologies. Typically, the foundational layer is constructed as a storage layer, encompassing one or more databases such as SQL, NoSQL, or NewSQL. This layer assumes responsibility for the storage, retrieval, and management of data, including tasks like indexing, querying, and transaction management. 

To enhance this architecture, it is advantageous to design a data access layer that resides above the storage layer but below the service layer. This data access layer leverages technologies like object-relational mapping or data access objects to simplify data retrieval and manipulation. Finally, at the topmost layer lies the presentation layer, where the information is skillfully presented to the end user. The effective transmission of data through the layers of an application, culminating in its presentation as meaningful information to users, is of utmost importance in a modern data architecture. 

The goal here is to design a scalable database with the ability to handle a high volume of traffic and data while minimizing downtime and performance issues. By following best practices and addressing a few challenges, we can meet the needs of today's modern data architecture for different applications.

Figure 1: Layered architecture 

Considerations

By taking into account the following considerations when designing a database for enterprise-level usage, it is possible to create a robust and efficient system that meets the specific needs of the organization while ensuring data integrity, availability, security, and scalability. 

One important consideration is the data that will be stored in the database. This involves assessing the format, size, complexity, and relationships between data entities. Different types of data may require specific storage structures and data models. For instance, transactional data often fits well with a relational database model, while unstructured data like images or videos may require a NoSQL database model. 

The frequency of data retrieval or access plays a significant role in determining the design considerations. In read-heavy systems, implementing a cache for frequently accessed data can enhance query response times. Conversely, the emphasis may be on lower data retrieval frequencies for data warehouse scenarios. Techniques such as indexing, caching, and partitioning can be employed to optimize query performance. 

Ensuring the availability of the database is crucial for maintaining optimal application performance. Techniques such as replication, load balancing, and failover are commonly used to achieve high availability. Additionally, having a robust disaster recovery plan in place adds an extra layer of protection to the overall database system. 

As data volumes grow, it is essential that the database system can handle increased loads without compromising performance. Employing techniques like partitioning, sharding, and clustering allows for effective scalability within a database system. These approaches enable the efficient distribution of data and workload across multiple servers or nodes.

Data security is a critical consideration in modern database design, given the rising prevalence of fraud and data breaches. Implementing robust access controls, encryption mechanisms for sensitive personally identifiable information, and conducting regular audits are vital for enhancing the security of a database system. 

In transaction-heavy systems, maintaining consistency in transactional data is paramount. Many databases provide features such as appropriate locking mechanisms and transaction isolation levels to ensure data integrity and consistency. These features help to prevent issues like concurrent data modifications and inconsistencies.

Challenges

Determining the most suitable tool or technology for our database needs can be a challenge due to the rapid growth and evolving nature of the database landscape. With different types of databases emerging daily and even variations among vendors offering the same type, it is crucial to plan carefully based on your specific use cases and requirements. 

By thoroughly understanding our needs and researching the available options, we can identify the right tool with the appropriate features to meet our database needs effectively. 

Polyglot persistence is a consideration that arises from the demand of certain applications, leading to the use of multiple SQL or NoSQL databases. Selecting the right databases for transactional systems, ensuring data consistency, handling financial data, and accommodating high data volumes pose challenges. Careful consideration is necessary to choose the appropriate databases that can fulfill the specific requirements of each aspect while maintaining overall system integrity.

Integrating data from different upstream systems, each with its own structure and volume, presents a significant challenge. The goal is to achieve a single source of truth by harmonizing and integrating the data effectively. This process requires comprehensive planning to ensure compatibility and future-proofing the integration solution to accommodate potential changes and updates. 

Performance is an ongoing concern in both applications and database systems. Every addition to the database system can potentially impact performance. To address performance issues, it is essential to follow best practices when adding, managing, and purging data, as well as properly indexing, partitioning, and implementing encryption techniques. By employing these practices, you can mitigate performance bottlenecks and optimize the overall performance of your database system. 

Considering these factors will contribute to making informed decisions and designing an efficient and effective database system for your specific requirements. 

Advice for Building Your Architecture

Goals for a better database design should include efficiency, scalability, security, and compliance. In the table below, each goal is accompanied by its corresponding industry expectation, highlighting the key aspects that should be considered when designing a database for optimal performance, scalability, security, and compliance. 

Table 1

When building your database architecture, it's important to consider several key factors to ensure the design is effective and meets your specific needs. Start by clearly defining the system's purpose, data types, volume, access patterns, and performance expectations. Consider clear requirements that provide clarity on the data to be stored and the relationships between the data entities. This will help ensure that the database design aligns with quality standards and conforms to your requirements. 

Also consider normalization, which enables efficient storage use by minimizing redundant data, improves data integrity by enforcing consistency and reliability, and facilitates easier maintenance and updates. 

Selecting the right database model or opting for polyglot persistence support is crucial to ensure the database aligns with your specific needs. This decision should be based on the requirements of your application and the data it handles. 

Planning for future growth is essential to accommodate increasing demand. Consider scalability options that allow your database to handle growing data volumes and user loads without sacrificing performance. Alongside growth, prioritize data protection by implementing industry-standard security recommendations and ensuring appropriate access levels are in place and encourage implementing IT security measures to protect the database from unauthorized access, data theft, and security threats. 

A good back-up system is a testament to the efficiency of a well-designed database. Regular backups and data synchronization, both on-site and off-site, provide protection against data loss or corruption, safeguarding your valuable information. To validate the effectiveness of your database design, test the model using sample data from real-world scenarios. This testing process will help validate the performance, reliability, and functionality of the database system you are using, ensuring it meets your expectations. 

Good documentation practices play a vital role in improving feedback systems and validating thought processes and implementations during the design and review phases. Continuously improving documentation will aid in future maintenance, troubleshooting, and system enhancement efforts. 

Primary and secondary keys contribute to data integrity and consistency. Use indexes to optimize database performance by indexing frequently queried fields and limiting the number of fields returned in queries. Regularly backing up the database protects against data loss during corruption, system failure, or other unforeseen circumstances. 

Data archiving and purging practices help remove infrequently accessed data, reducing the size of the active dataset. Proper error handling and logging aid in debugging, troubleshooting, and system maintenance. Regular maintenance is crucial for growing database systems. Plan and schedule regular backups, perform performance tuning, and stay up to date with software upgrades to ensure optimal database performance and stability. 

Conclusion

Designing a modern data architecture that can handle the growing demands of today's digital world is not an easy job. However, if you follow best practices and take advantage of the latest technologies and techniques, it is very much possible to build a scalable, flexible, and secure database. It just requires the right mindset and your commitment to learning and improving with a proper feedback loop. 

Additional reading: 

• Semantic Modeling for Data: Avoiding Pitfalls and Breaking Dilemmas by Panos Alexopoulos
• Learn PostgreSQL: Build and manage high-performance database solutions using PostgreSQL 12 and 13 by Luca Ferrari and Enrico Pirozzi
• Designing Data-Intensive Applications by Martin Kleppmann

This is an article from DZone's 2023 Database Systems Trend Report.

For more:

Read the Report