As we as a society become more reliant on data, the need for efficient data warehouses and databases is growing significantly. Every entity - from large enterprises to home users - stores data in one format or another. For enterprises, data warehouses and data lakes become the solution for storing vast sums of data over a long period of time.

Simply storing data for later processing, however, is no longer sufficient. Companies are starting to suffer from high data storage costs and slow queries due to inefficient data warehouses. At the same time, poor database design is also the root cause of low-quality insights. Is it time to pay more attention to database design for long-term sustainability?

Planning Is Everything

Jumping straight to data collection without sufficient planning is one of the reasons why a lot of databases are not efficient or sustainable. It is very tempting to jump straight to data collection when you have a lot of cloud computing resources at your fingertips. SaaS (News - Alert) billing and large storage options provided by companies like Amazon certainly make planning look less important.

In reality, a poorly structured database will continue to cause problems in the long run. Being able to identify the kind of data that you want to collect and store, how to sufficiently separate noise from valuable data, and of course how data must be structured is crucial. Trying to do these things once you have a lot of data in the warehouse is incredibly difficult.

Pre-Process and Normalize

Another thing that helps keep data warehouses efficient is pre-processing. Rather than storing a large sum of raw data, it is sometimes more efficient to pre-process data as they are being captured. At the very least, you want to normalize certain things and make sure that the structure of the data remains consistent across periods.

Normalizing data before they are being stored is also a good way to keep queries efficient. Rather than using JOIN in your SQL queries, it is often more efficient to build better relational databases through indexes and substrings. The goal here is to maintain a searchable database, even when the size of the database is incredibly large.

Standardized Structure

That actually brings us to the next point, which is making sure that the structure of the database is standardized. A lot of database engineers tackle this issue early, knowing that a bad structure can affect a lot of things further down the line. Unfortunately, many data warehouses still use duplicate names and bad structures.

When you have row names that are similar to each other, for instance, doing a search query can be painful. Table names are the same. Of course, you also want to build relations between tables rather than storing data in random ones – adding transaction rows to a USER table is a good example, and one that happens a lot in corporate data warehouses.

Visualize Use Cases

Before we get any further, it is also important to understand how the design of a database will affect how easy it is to utilize in various use cases. This is why you have to think about the queries you will be running and whether further data processing is required when designing a data warehouse. You can add some design tweaks that make the data warehouse more efficient when used.

Computer science graduate students are now studying algorithms and database design as a complete set, giving them the ability to structure database systems as well as the application that will run on top of it. Top master’s in computer science online programs also cover wider use cases and take into account factors such as security and database health.

Manage Domain Values

A big misconception in database design relates to the management of domain values. One generic domain table should connect all of the domain values, right? Well, not really. When you have the domain values of a table managed by another table with generic domain values, you will end up with JOIN … as … commands – many of them – in every query you run.

Unless the JOIN is natural, you should rethink the relationship between tables in your database. Rather than using generic domain values, try to create unique ones for each table, and then build relationships between tables in a contextual way. This allows you to use foreign key constraints more naturally and significantly shorten your queries.

Utilize Labels!

One tool that is always underutilized in database design is labels. Did you know that you can label tables and columns easily in almost all database frameworks and platforms? Adding labels lets you understand the data that you’re working with better and faster. Rather than trying to remember what CustRepID represents, you can simply use CustomerReportID.

Labeling, combined with a proper structure as we discussed earlier, lets you keep your database more sustainable too. When you need to add new fields or attach new tables to the framework, you know exactly how to structure the new set of data. On top of that, you don’t have to open the database documentation every time you need to design a new query.

Update the Documentation

That said, having proper documentation for the design of the database is still very important. Naming conventions, query standards, fields and tables, and relationships must be documented for long-term maintenance of the data warehouse. The more detailed your database documentation is, the easier it will be to keep the database healthy for longer.

Documentation also helps when new administrators must get accustomed to the data warehouse. Rather than relying on a handful of individuals who designed the database, you can have a more agnostic database design. Once again, this simple tip makes your database warehouse more sustainable and valuable in the long run.

That brings us back to the initial question. Is it time to pay more attention to database sustainability? Considering the volume of data that we store today, the answer to this question is a definite YES. You don’t have an infinite cloud resource to work with, and the tips we discussed in this article will help you maintain a healthy database through proper database design.