6 Steps for Best Practices in Database Design
When building a database, adhering to the following best practices will ensure that the final product is both useful and easy to use.
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When designed correctly, databases are incredibly powerful tools for recording, storing, retrieving, and comparing data. However, a database designed without care and purpose isn’t just ineffective, it can be a real hindrance to those who use it most. When building a database — regardless of its ultimate purpose — adhering to the following best practices will ensure that the final product that is both useful and easy to use.
1. Table Structure and Purpose
There are a number of techniques available today, such as Scrum and RAD (rapid application development), which help IT teams to develop databases at a rapid pace. When speed and efficiency are being leveraged by institutions to force a quick build, it can be tempting to dive straight into constructing a database with only a vague notion of what the outcome needs to be.
While it can often feel as if efficiency is being emphasized, this is, in fact, an illusion. Work might get off the starting line quicker, but instead, rushing ahead will greatly increase the chances of stumbling further down the line with database issues. When that happens, devs will have to spend a significant amount of time unpicking and reshaping what has already been done.
Have a purpose for the database that goes beyond simply storing information. For example, consider where the database is to be deployed. Will it support a POS system so you can gather data about your customers’ behavior? Or is it to help manage customer relations? The more clearly your team is able to define the databases’ purposes, the smoother the design process will go.
2. Choose the Right Data Modeling Software
Whether building an application or a database, it’s best to use the right tools for the job. In the case of database design, there are many online tools available now. Choose ones such as Lucidchart, Draw.io, and Microsoft Visio, which all support database entity design. The whole point of leveraging data modeling is to visualize the complexity and be able to spot shortfalls where improvements can be made.
Visualizing the design of a system makes it easy to communicate the plans to colleagues. Such widespread communication ensures that everyone involved in the database construction is on the same page from the very beginning, which will help to streamline the entire development process.
3. Outline the Requirements
Clearly outline the foreseeable future of the software project and the corresponding database. Plot as much as you can while allowing for flexibility, especially in a startup space.
It is important to look beyond the simple technical necessities. So, requirements should consist of more than a list of hardware and software. As important as these things are, they are not what is pivotal to producing good database designs. Have a plan in place to support changes and deviations as the software and database are being developed. Many companies support non-destructive changes only when it comes to adapting design, and changes are then only supported by an automated migration mechanism.
Work with the team, department or organization that the database will ultimately be supporting, and ask them for their input. Make sure to speak to people of all levels, from the frontline staff, right up to the managers. It is only with the full picture this feedback provides will you gain a firm grasp of the requirements.
Inevitably, you will encounter conflicting needs from different people within the same business, team or department. When this occurs, use your best judgment, based on past experience to try and usher in some form of compromise which satisfies all parties—without undermining the ultimate purpose of the database.
4. Model the Data with Care
A comprehensive guide to data modeling is a subject worthy of several articles. However, there are a few key points which, when adhered to, will allow you to model data in the most appropriate way.
Depending on the purpose of the database, you will need to decide whether it’s more appropriate to use transactional modeling or dimensional modeling techniques. If the database is to be an online transaction processing (OLTP) database, use transactional modeling. If, on the other hand, the database is to be relational, turn to dimensional modeling.
All database data modeling should ascribe to conceptual (CDM), logical (LDM), and physical (PDM) data models. The CDM is all about semantics (meaning); it is this that defines the scope of the final database. Rather than discussing the fine details of how the database will work, the CDM describes the entities and data types it will include, as well as the relationships between them.
The LDM can be thought of as the implementation of the CDM, and it, in turn, will assist in producing the PDM. The LDM helps to put together a roadmap for designing the physical database; it does this by defining the business’ data entities, and the rules that govern the relationships between them. This encompasses criteria such as data types, key status, and attribute set details, among others.
Finally, the PDM defines the way in which data will be physically stored. It is produced by mapping the LDM to the chosen database management system (DBMS) platform.
5. Use Appropriate Data Types
Using the wrong data type for any given attribute can have a number of negative consequences on the finished database. It can lead to bloated file sizes, less accurate data, difficulty in joining data tables together, and difficulties syncing attributes with the application layer.
Using the most appropriate data type for an attribute is part of domain integrity. Enforcing domain integrity rules will ensure that attributes can only contain data types which are valid for that attribute. For example, if the database records people’s ages, ensure whole integers of 3 digits max.
Most data modeling software allows you to create and define individual data domains. These can be used to not only ensure that the correct data type is used, but also that a value must be given and a null value is rejected.
6. Define or Adopt a Style Guide
This section is all about establishing database metadata naming conventions. When it comes to tables, indexes, foreign keys, triggers, functions, etc., setting up clear and consistent guidelines for all the stored information will save you time, energy, and money in the long run.
While there is much out there on the internet about how you should and shouldn’t adhere to particular naming conventions, stick with these four rules and you can’t go wrong:
- Any form of naming convention is better than none.
- There’s no such thing as a “true” standard, everyone has their own preferences.
- If there are rules in place already, stick with those rather than trying to reinvent the wheel.
- Best practice is always persistence when it comes to ongoing database maintenance.
By following these 6 steps for best practices in database design, you will ensure that every database you build is fit for purpose. Once these rules become second nature, so will excellent database performance. While it may sound like a lot to achieve, if you establish these guidelines from day one of the build, you’ll save yourself from a bunch of headaches in the future.
Don’t forget to read our article on SRE vs. DevOps: Any Common Ground?
Published at DZone with permission of JP La Torre. See the original article here.
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