Database Design & Database Architecture: Why They Are Crucial

Data has become one of the biggest assets for businesses. It’s no longer just about collecting data but also about utilising it effectively to drive business growth. 

If you’re a business owner, you know how important data is; it’s the lifeblood of any successful organisation and a modern fuel for digital transformation. But what many organisations don’t often realise is that data also needs an organised system in order to be useful and effective. This system encapsulates the principles of database design & architecture—a crucial piece of technology that stores, structures, and organises your corporate data into something manageable. 

Without a solid foundation for your tech setup, it can be difficult (if not impossible!) to access and use information effectively, leaving you lagging behind the competition and struggling just to keep up with the times. That’s why understanding database design & architecture has become so important: without it, companies may find their operations hindered by inadequate storage solutions or bogged down by sluggish retrievals and analytics processes due to inefficient coding practices. 

In this blog post, we’ll explore why having a comprehensive database design & architecture strategy in place can help ensure success – now more than ever!

What Is Database Design? 🎨

Database design is the process of creating a blueprint for organising data into a structured and efficient database. The design process involves identifying the data that needs to be stored, defining relationships between data elements, and creating an efficient and scalable structure for storing the data.

This relationship between the data elements is also commonly referred to as database schema. Database schema not only tells us about the dependencies between data points but also helps to identify suitable data types and storage processes for said data.

Database design is essential for the success of any Database Management System (DBMS). Careful thought and consideration when designing a database can be rewarded with efficient performance, scalability, and data accuracy – while neglecting to plan ahead properly might cause lags in your query performance and lead to other problems such as redundant data storage and data inconsistency.

✍ Database Design Best Practices

If you want to implement proper database design practices in your company, then here are 10 tips that you need to keep in mind.

1. Use Primary Keys: Each table in a database should have a unique primary key that identifies each record. This ensures that the data is organised and easily accessible.
2. Normalise Your Data: Normalisation is the process of dividing your data into smaller, relatable tables to improve clarity and reduce redundancies within your dataset. Organising data through normalisation can also improve data integrity, which in turn, makes your data more consistent and accurate.
3. Use Foreign Keys: Foreign keys are used to link tables in a database to improve data integrity and consistency.
4. Avoid Redundant Data: Redundant data can lead to data inconsistencies and errors. So, It’s important to avoid storing the same data in multiple places.
5. Use Indexes: Indexes help to speed up database queries by allowing the database to quickly find the data it needs. Make sure to determine the data elements that require querying if you intend to improve query performance.
6. Use Constraints: Constraints help to ensure data integrity by preventing invalid data from being entered into the database.
7. Use Data Types Appropriately: Choosing the right data type for each field in a database can help to ensure proper data storage and retrieval.
8. Use Meaningful Names: Meaningful and descriptive names for tables, fields, and other database objects make it easier to understand and maintain the database.
9. Document Your Database: Properly documenting your database design, including tables, fields, relationships, and constraints, can help others to understand and use the database effectively.
10. Plan For Scalability: Designing a database that can scale as the size of the data and the number of users grows can help to ensure that the database remains efficient and effective over time. 

What is Database Architecture? 🏛️

Database architecture refers to the overall design and structure of a database system. It encompasses everything from hardware and software components to network infrastructure and security measures. It includes the structure and layout of the data, the relationships between the data, the procedures and rules for accessing and manipulating the data, and the physical and logical components of the system.

Database architecture can be divided into three levels: conceptual, logical, and physical. The conceptual level defines the overall structure of the database, including the entities (objects or concepts) and their relationships. The logical level defines the specific data elements and their relationships, and the physical level defines how the data is stored and accessed.

Database architecture is critical to the performance, scalability, and reliability of a database system. A well-designed database architecture can ensure efficient data retrieval and manipulation, minimise data redundancy, and support data integrity and security.

📐 Database Architecture Best Practices

In our opinion, the following are 10 database architecture principles that you must follow if you want to build a database system that is primed for peak performance and longevity.

1. Scalability: A good database architecture system should be able to handle a large amount of data and users. This can be achieved through techniques such as sharding, replication, and partitioning.
2. High Availability: Databases should be available 24/7, and downtime should be minimal, if any. Techniques such as load balancing, clustering, and failover are popular in this regard.
3. Peak Security: A good database architecture should be designed with security in mind, including access control, encryption, and auditing.
4. Backup & Recovery: Databases should be backed up regularly and have a well-defined recovery plan in place to ensure that data can be restored quickly and reliably in the event of a disaster.
5. High Performance: A good database architecture should be designed for optimal performance, including efficient query execution, data caching, and data compression.
6. Modularisation: A good database architecture should be modular, with separate components for data storage, data processing, and data presentation. Modularisation makes it easier to scale and maintain the database over time.
7. Data Model: The data model should be designed to accurately represent the business requirements and ensure that data is consistent and accurate.
8. Data Integration: A properly set up database architecture system should support data integration with other systems, including data warehouses, ETL tools, and BI platforms.
9. Monitoring & Management: Databases should be monitored and managed proactively, including monitoring for performance issues, security vulnerabilities, and data corruption.
10. Documentation: You should also ensure your database architecture is well-documented, with detailed descriptions, data models, and data flow. This makes it easier for developers and administrators to jump in when needed and understand and maintain the database.

Steps In The Database Design Process 🌀

🔍 Identify The Purpose

Before designing a database, you need to identify the purpose of the database and the type of data it will store. This will help you determine the tables and fields needed in the database.

📖 Define The Entities & Relationships 

Identify the entities (objects) that will be stored in the database and the relationships between them. This will help you determine the tables needed in the database and the fields in each table.

📂 Normalise The Data 

Normalisation is the process of organising data in a database to reduce redundancy and dependency. This improves data consistency and reduces the risk of data anomalies.

🗳️ Choose Appropriate Data Types

The selection of appropriate data types for each field in the database cannot be overlooked. This process will ultimately help remove redundancies and facilitate efficient storage of data.

🗝️ Use Primary Keys

Each table in the database should have a primary key. This is a unique identifier for each record in the table. Without primary keys, you’ll have a difficult time maintaining the clarity and integrity of your database.

🗃️ Create Indexes

Indexes are used to speed up queries in the database. You should create indexes for fields that are frequently searched or sorted.

⚡ Consider The Performance 

Design the database with performance in mind. This includes optimising the database schema, using appropriate indexing, and minimising the number of joins required in queries.

🌱 Plan For Growth

Plan for future growth by designing the database to accommodate future data needs. Remember, a database that is not structured for growth at scale isn’t following ideal database design and architectural practices.

Types Of Database Management System (DBMS) Architecture 💻

1️⃣ Single-Tier Architecture 

An optimal choice for developers needing a straightforward solution, one-tier architecture is an efficient and effective way to streamline applications. By keeping all the elements – from interface to middleware and back-end data – on one server or platform, this model of development offers quick clarity with minimal complexity.

In this architecture, the DBMS software is installed on the same computer as the application that uses it. This is the simplest architecture and is commonly used for small applications.

2️⃣ Two-Tier Architecture 

In the two-tier Client-Server architecture, there is no middleman standing between the client and server – they communicate directly with each other. This streamlined setup offers an efficient application environment capable of processing requests quickly.

The DBMS software is installed on a separate server computer, and the application communicates directly with the database server. This architecture is usually preferred for client-server applications.

3️⃣ Three-Tier Architecture 

The 3-tier architecture is a powerful tool for maximising efficiency and clarity in how data interacts with users. It’s the most commonly used framework when it comes to designing DBMSs, allowing various applications like web services or distributed apps to benefit from its practicality – especially across multiple systems. Its unparalleled strength means you’ll never be far away from that extra level of accuracy needed on tough projects.

The DBMS software is installed on a separate server computer, and the application is split into two parts: a client-side application and a server-side application. The client-side application communicates with the server-side application, which communicates with the database server. This architecture is most suitable for web-based applications.

♑ N-Tier architecture 

This architecture extends the three-tier architecture by adding additional layers for scalability and flexibility. The application is divided into multiple layers, each with its own set of responsibilities. The layers are – the collection layer, storage layer, processing layer, analytics layer, and application layer, from the bottom to the top.

In traditional MVC (Model-View-Controller) frameworks, the Application’s Structural components are logically separated. On the other hand, an N-Tier Architecture is physically distinct, where each layer communicates linearly and passes data through a central processing tier. The interaction of all elements occurs at two key points – between Controller & Model as well as View & Model in an interweaving triangle of communication. This architecture is a popular choice for enterprise-level applications.

🤝 Peer-To-Peer architecture 

There is no central server or database. Each computer in the network has its own copy of the database, and they all communicate with each other to synchronise changes. This architecture is preferred for distributed databases and peer-to-peer file-sharing applications.

Benefits Of Good Database Design & Architecture ✨

A meticulously crafted database design can be a big competitive difference. It isn’t enough to merely have one – it needs to be structured and efficient while being flexible enough to meet an organisation’s particular demands. But what do you get from having a properly organised infrastructure in place? Let’s take a look at some of the key benefits of following proper database design and architecture practices.

• Improved Data Integrity: A well-designed database ensures that data is accurate, consistent, and up-to-date. This leads to improved data integrity and eliminates errors caused by inconsistent or duplicated data.
• Faster Data Retrieval: A database that is well-designed allows for faster data retrieval, making it easier to find and access the information you need.
• Grow At Scale: Your database will also be set up properly to scale easily as your business grows, allowing you to store and manage more data without sacrificing performance.
• Blazing Speed: A well-designed database architecture ensures that data can be accessed quickly and efficiently, even during peak usage periods.
• Security: The best database design and architecture practices include robust security measures that protect sensitive data from unauthorised access.
• Flexibility: A great database architecture can be easily adapted to changing business needs, ensuring that the database system can evolve alongside the organisation.

Why Is Database Design & Architecture Important? 🤔

⚖️ Improved Decision Making

A well-designed database with a sound architecture provides businesses with easy access to accurate and up-to-date data. This data can be used to make informed decisions that drive business growth and improve overall performance.

🕗 Increased Efficiency

Efficient implementation of good database design and architecture practices can improve the speed at which data is retrieved and processed. This leads to faster decision-making, reduced downtime, and increased productivity.

💸 Cost Savings

A poorly designed database can result in higher maintenance and operating costs. You don’t want to be mending your database every time you need to use it in your applications. A well-designed database, on the other hand, can reduce costs by optimising hardware and software resources and reducing crashes and bugs, and the need for additional technologies.

🎯 Competitive Advantage

A database with a sound architecture can provide businesses with a competitive advantage by improving their ability to analyse and utilise data. Let’s face it despite knowing the importance of database design and architecture, not many companies prioritise database architecture at a high level. So, take advantage of that and step ahead of the curve. 

Common Techniques For Database Design ⚙️

The two most commonly used database design techniques are normalisation and entity-relationship (ER) modelling. Let’s take a closer look at both of these ideologies.

🗄️ Normalisation

As we’ve already discussed multiple times in this article, normalisation is a database design principle that cannot be overlooked. This process involves organising data in a database to reduce useless data and unneeded dependency. Normalisation provides a systematic database design process for identifying and eliminating data redundancy from your database to improve performance, accuracy, and consistency. 

With this powerful approach, you can store complex information in a tabular form while ensuring that the relationships between columns of data make sense logically. Put simply – it ensures useful rather than redundant records are stored. This means no more useless duplication or potential anomalies due to insertion, update or deletion.

What if your database is not normalised? Normalisation is essential for any database – without it, insertions and updates could put your data at risk. And forget about simple deletion anomalies; they become a regular occurrence when databases aren’t normalised.

There are several normal forms, each with its own set of rules. Each higher normal form aims to reduce data redundancy further. Despite the various forms of data normalisation, they all seek to achieve one objective – reducing data redundancy and improving data consistency.

First Normal Form (1NF)

A first normal form database has to satisfy two conditions – contain only atomic values, and contain no repeating groups. So, what are atomic values? Atomic values are data values that cannot be divided. While a repeating group is a table that contains two or more columns that are closely related to each other.

Second Normal Form (2NF)

A database in the second normal form also has to fulfil two special conditions – should be in the first normal form and make sure all non-key data attributes are fully functionally dependent on the primary key.

How does this happen, you ask? Let’s take an example. Say you have a database table with attributes X and Y. If attribute Y is functionally dependent on X but not functionally dependent on a subset of X, then Y is fully functionally dependent on X. The point is, in a 2NF table, all non-key attributes mustn’t be dependent on a subset of the primary key.

Third Normal Form (3NF)

A database entity relation is considered in the third normal form if it is in the second normal form and no-non key attributes are transitively dependent on the primary key.

To understand this better, let’s use another example. Say, a company uses the following relation to store its customer data.

Customer (ID, Name, Location, Account_No, Account_Name)

Here, the attribute “ID” is the identification key, and all attributes are single-valued (1NF), and say, the table is also in 2NF. After normalisation, you’ll notice the following dependencies.

• The attributes Name, Location, and Account_No are functionally dependent on the primary key, ID.
• And Account_Name is functionally dependent on Account_No.

How did this happen? Well, to get to the third normal form, we had to put Account_Name in a separate table altogether with the Account_No attribute to identify it. This formed a transitive dependency between the two, and this was possible because Account_No was not a primary key of the original relation when the datasets were in 1NF and 2NF.

👩‍💻 Entity-Relationship (ER) Modelling

ER modelling is a technique used to visually represent the entities and relationships in a database. ER diagrams include entities (objects), attributes (fields), and relationships between entities. The relationships can be one-to-one, one-to-many, or many-to-many. ER, modelling helps to identify the tables and fields needed in the database and to ensure that relationships between entities are properly defined.

Other techniques for database design include object-oriented database design, which involves modelling data as objects rather than tables, and data warehousing, which involves organising data for analysis and reporting.

Conclusion: Database Design & Database Architecture 📋

In conclusion, database design and architecture play a crucial role in the success of modern businesses. A well-designed database with a sound architecture allows businesses to access, analyse, and utilise data efficiently, leading to improved decision-making, increased efficiency, cost savings, and competitive advantage.

To wrap it all up, having an effective and optimised database design and architecture are essential for thriving businesses. If you’re still using outdated systems or manual processes to store and access your data, now is the time to make a change. Your customers will thank you for it!

Whether you’re just starting out or have been around the block, our team of database engineering and design specialists can help drive your business forward with custom database management solutions tailored to fit your needs. Book a discovery call today to discuss how you can take advantage of the power of data in improving customer experiences.