On the surface, it may seem like database design is not very critical. However, the database is the backbone that strengthens your technology significantly. Not optimizing database design architecture makes slow and inefficient websites. As a result, Google penalizes unoptimized low-speed sites by lowering their SEO ranking. So, sites end up losing their abilities to gain organic search traffic and generate leads for their businesses. Additionally, if your data is scattered, it complicates the task of modification for your database managers. In this post, we examine database design best practices in detail to design better databases.
Selecting the right database type is critical to the entire database design process. Currently, we have two different types of databases. The first one focuses on the query language that databases use to define and modify the data. Presently, SQL-based databases are popular options to handle structured data. However, NoSQL databases seamlessly integrate with machine learning, web analytics, and the Internet of Things (IoT).
Such databases have greater flexibility, scalability, and speed. Another way to segregate them is based on data models as relational, hierarchical, network, and object-oriented databases. So, businesses need to understand different database types and pick the right one for your application.
When the database in your development system differs from the production system, it complicates database design. Ideally, most companies use the development environment to test any new change exhaustively before moving it to production. However, due to urgent issues that need timely action, companies may alter the production environment directly.
When these systems are out of sync, future changes on the same object passing from the development override the previous change. In such cases, the urgent issue pops up again and causes trouble. Companies rarely take the time out to document these emergency changes to the prod system. Hence, it makes it even more difficult for them to fix the issue yet again.
The most effective way of dealing with this issue is by preventing it from occurring. Through fail-proof safety nets, you can easily detect config drift and evaluate if the code is safe to deploy. They also protect your production systems from corrupting by identifying when it’s safe to merge both systems. Database design principles recommend you focus on preventing this issue instead of dealing with it when it gets worse.
Young man engineer making program analyses In most cases, database designers need to create structures that assist companies until a significant time in the future. So, they need to know what the data represents, how it’s acquired, and how much volume of data they’ll receive. They’ll also have to consider how their end-users will use the data to design the most effective databases.
So, designers need to understand where the data is coming from to design good systems for their clients. The way companies collect data affects its volume as well. As a result, they need to consider all these factors to make the database more efficient and usable.
The purpose of data also influences normalization levels, data structures, record sizes, and the entire general system implementation. When you ignore these factors, you defy design basics and make your systems less usable.
Designers need to use the right tools to model your database to visualize the complexity of the design effectively. After understanding the design properly, they can detect shortfalls and adjust through improvements accordingly. Through efficient tools like Lucid chart and Microsoft Visio, you can easily communicate plans to your colleagues. With clear communication, designers ensure their team is on the same page about the entire development process.
Before designing the database, a designer must understand the foreseeable future of that software project and its database. You can plan as much as possible and also allow some flexibility if you’re working for a startup. In addition to technical necessities, the design also needs to be flexible enough to accommodate changes and deviations when the database develops. So, work with the concerned teams and organizations that’ll use the database daily to get valuable inputs. Ensure you ask people from different levels to get a clearer picture of the requirements.
Naturally, you’ll receive conflicting needs when you contact a lot of people. So, take the final call based on your experience and intuition by finding a good compromise that meets most of the needs.
When companies have more duplicate data, it complicates the developers’ job significantly. They need to construct business logic to keep the multiple versions of the same data up-to-date. Companies need to adhere to normalization rules religiously to avoid these issues. Some bad outcomes of redundancies include:
Though, in some cases, data redundancy is needed. So, if organizations opt for redundancy in their processes, they need to document it. This documentation helps them change their systems in the future easily.
Stored procedures include prepared code that can be tweaked and used by organizations. By not using these procedures, organizations waste their valuable time and resources. If there’s an SQL query that your employees run daily, instead of writing it daily, you can create procedures. Your employees can use it and get the output quickly. Initially, you’ll have to spend time developing them for your employees. However, when you spend time preparing and documenting them, your end-users will benefit immensely.
Modern databases need to be extremely agile, flexible, scalable, and useful to organizations. These demanding requirements make the designer’s work even more difficult. Moreover, organizations need data to drive their decisions and boost their revenue. With a better structure and easier access, data becomes a powerful tool to help businesses. We hope these best practices help you improve your database design and leverage data to gain a competitive edge.
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