What is data warehouse design?

In today's data-driven world, a data warehouse has become an essential part of business. These single data repositories are a great option for storing information from multiple data sources, as they allow companies to centralize data and ensure its availability for analytical processing. As beneficial as it can be to have a data warehouse, integrating it into your infrastructure can be a complex ordeal, especially when you are designing it.

The data warehouse design process presents numerous challenges and potential pitfalls, as well as constantly evolving requirements. This means that designing a data warehouse is an ongoing process to improve the way the system extracts, transforms and loads data collected from various sources. Given this enormous complexity, there are many aspects to take into consideration before building your own data warehouse.

What are the main components of a data warehouse?

When building a data warehouse, it is important to think about its architecture. This is because you need to take into account the essential components of these systems to be able to make the most of them. Here are the critical elements you need to design a data warehouse architecture:

• Data Source Layer : The sources from which you collect data, including internal (ERP, CRM, etc.) and external sources (social media, public databases, etc.)
• Staging area : The temporary storage where all collected data is consolidated before being stored
• Data storage layer : the database where you keep structured data and the data mart where you provide the data for analysis and reporting
• Business Analytics and Intelligence : The online business analytical processing tools that query, extract, and evaluate data to create reports and visualizations

These elements can be organized following 2 data warehouse design approaches:

Top-down approach

The data source layer sends structured, semi-structured, or unstructured data to the staging area, where all data is cleaned. After that, the cleaned data goes to the data warehouse and from there it is divided into as many data marts as there are specific functions in a company.

Bottom-down approach

The main difference from the top-down approach is that the clean data goes to the data marts before it goes to the data warehouse. This makes reporting on specific functions faster, although the dimensional view of data marts is not as consistent as the top-down approach.

What are the steps to design a data warehouse?

Although not all data warehouse design processes look the same, there are several steps common to most of them. They will look different depending on the data sources, the complexity of the desired results, and the overall complexity of the system. However, the main steps can be summarized as follows:

Requirements definition

The first step is to determine the business needs, objectives and expectations surrounding the data warehousing project.

Exploration and conceptualization

Here, the team explores data sources and the overall level of security with the aim of understanding users. Next, engineers begin designing the data warehouse, choosing the ideal architecture and deployment type.

Planning After preparing the initial draft, the team begins to adequately define the scope, deliverables and roadmap of the project, taking into account the available resources, budget and risks.

In-depth analysis of technology and data sources

Here, the engineering team delves into the available platforms to build the storage solution. Additionally, developers thoroughly analyze data sources and define the process to extract, transform, and load data into the data warehouse.

Data modeling

Here, the team chooses one of the most common data models for the warehouse and data marts. Options include star, snowflake, and galaxy schemes.

Data warehouse development

After all aspects of the project are defined and agreed, the engineering team begins working on the solution, connecting data sources to databases, creating data marts, implementing ETL processes and testing the entire system.

Deployment and maintenance

Once the development is complete, the team launches the solution to all users, closely monitoring performance, solving problems that may arise and adjusting different parts to ensure data availability, quality and security.

Data Warehouse Design Best Practices

Considering how complex it actually is to design a data warehouse, it's always a good idea for the team to have a set of best practices in mind. By following these guidelines, the engineering team can avoid the most common mistakes in this type of project and at the same time speed up the entire development process.

• Correctly define the data model. You always need to know what kind of data you are collecting and how you can clean and store it for better analysis.
• Build a data flow diagram. Understanding where all your data repositories and data marts are and how they handle information coming from their sources can help you refine your data-driven operations.
• Use a standard data warehouse architecture. Using a well-known and tested architecture can increase your efficiency and provide a clearer way to maintain and update your data warehouse.
• Break your data warehouse projects into smaller parts. Adopting an agile methodology is critical when designing a data warehouse, as you will be able to achieve faster delivery of valuable parts of the system. Plus, you'll be able to evolve the system faster as your needs and data change.
• Automate your data storage. You can use various automation tools to clean data, enforce coding standards, and scale up or down.
• Consider using a cloud-based environment. You no longer need to use an on-premises warehouse for your data. Instead, you can choose one of the many cloud-based alternatives to speed up the process and access greater flexibility.

Data storage schemes

The schema is the logical description of the database, which includes the name and description of all record types. However, data warehouses do not use the relational model often seen in databases. Instead, data warehouses use one of the three most common schemas for storage, namely:

Star Schema #1

A fact table is at the center of a star-shaped arrangement and is surrounded by as many associated dimension tables as necessary.

#2 Snowflake Scheme

Based on the star schema, the snowflake schema adds additional dimension tables to each dimension table present in a star schema.

#3 Galaxy scheme

Here, there are 2 fact tables that use and share the same dimension tables.

BairesDev helps your company create or improve data warehouse architecture

We have an elite team of data warehousing experts who can help you ideate, conceptualize, design, and architect your solution. We have years of experience across industries, providing complex, scalable storage platforms that can redefine the way you handle and manage your data. It doesn't matter what type of data warehouse you're trying to build, we can elevate its quality and deliver the results you're looking for.