How to Design a Data Warehouse?

Most people would think of a data warehouse as a place that stores information, which may or may not be used later on. But a data warehouse is much more than that; it’s about building a performance system that not just stores information but helps in decision making, catalyzes analytics, and helps in the long-term growth of a business. Real-life examples support this. Businesses that treat data warehouses as strategic assets, rather than just an IT initiative, end up reaping the benefits of this investment in the form of saved time, reduced costs, and unlocking valuable insights.

Whether you’re looking to build a data warehouse for the first time or looking to redesign or revamp a pre-existing data warehouse, this guide will help you understand how to design a data warehouse. This will support you in understanding data warehouses more deeply and walk you through the different components they comprise, best practices, and how they combine to help your business strategically.

What is a Data Warehouse?

So, what exactly is a data warehouse? For most people, a data warehouse would suggest a physical place full of servers that store data, and this is partly true. But in this modern age and digital landscape, a data warehouse performs a much bigger and more important role. In layman’s terms, a data warehouse is a centralized repository where data from various sources is collected, organized, and made available in a manner that enables efficient and effective analysis.

A company uses various applications and systems where data is entered and used for diverse purposes. A data warehouse collects all this data from the different sales systems, marketing tools, and ERPs. It organizes it into an effective and usable form for decision makers to use and make analyses without having to sift through piles and piles of unorganized and raw data.

There are some key characteristics all data warehouses should include, and these are:

Subject-orientation

It should be organized around common key business aspects, such as sales, customers, or finance.

Integration

Should comprise data from all sources, gathered into a consistent format.

Preservation

Data should remain stable once it is stored, and historical data should be preserved.

Time-variant

Should store historical data that can be analyzed and trends tracked over periods of time.

Benefits of Data Warehouse

A data warehouse can offer several benefits to a business, beyond just storing and preserving data. A properly designed and maintained data warehouse can transform how higher-ups in your business make decisions, how they access and analyze data, and eventually how they use that information. Here’s why successful businesses invest in data warehouses, and what you can expect in return by investing in one.

1. Centralized Data Management

A business has several levels of management within its workforce, all of which utilize separate and distinct applications and systems in their work. A CRM stores sales data, marketing performance is recorded using analytics tools, financial records are in ERP systems, and customer feedback is in support software. Since this data is scattered across multiple platforms, and there’s no definitive correlation between any sets of data, a more suitable system of gathering and analyzing this data is required. This is where data warehouses come in, and pull all the different sources of data into one central hub.

This enables everyone in a business, from the CFO to the marketing team, to work from a single, unified dataset. This eliminates any possibility of there being a “multiple versions of the truth” situation.

2. Better Decision-Making

The collection and presentation of data, done understandably and interactively, enable users of this information to interpret it and make decisions with great confidence. This is significant and significantly aids the decision-making process. Furthermore, this also eliminates the problem of having to go through different sources and compile the data to help make a decision.

3. Data Quality

By collecting and compiling data in a useful manner, the overall quality of the data is improved. Furthermore, the Extract, Transform, Load (ETL) process ensures that data is cleaned, standardized, and validated before it enters the warehouse. Having high-quality data of high quality is necessary as poor-quality data would lead to poorer decisions and vice versa. This is why ETL processes are important, as they fix common problems like inconsistent date formats, missing values, and duplicate records.

4. Historical Analysis

Since a data warehouse stores an enormous amount of data, there’s a lot of historical data preserved as well. This older data allows for be analysis of trends over months and even years, helping detect seasonal patterns and identify any changes in customer behavior.

5. Scalability

Nowadays, there are a lot of cloud-based warehouses as well, like Snowflake, BigQuery, and Amazon Redshift. These are different than the conventional physical data warehouses, and are designed to grow in size as your business grows. The storage and computing power offered can be scaled up on demand without incurring excessive costs for hardware upgrades.

What are the components of a Data Warehouse?

Several different components come together to ensure a data warehouse works efficiently. Understanding these components and how a warehouse is designed is necessary before jumping into the next phase.

Data Sources

These are the systems from which you pull data, including CRMs, ERPs, spreadsheets, and more.

ETL/ELT Process

Extract—Retrieve data from different sources.

Transform—Clean, format, and integrate data.

Load—Moving data into the warehouse.

There are two variations of this, and some organizations may use the other one, ELT (load first and transform later), as this offers faster performance.

Staging Area

This is a temporary storage space where data from all sources is stored before being transformed into useful information.

Data Storage Layer

This is the area where the cleaned, refined, and structured data is held. This can follow a star schema, snowflake schema, or a more complex architecture.

Metadata

Metadata refers to the information about your collected data, and this includes sources, definitions, and relationships, making it easier to understand and manage.

Data Marts

Data Marts are simply sets of data, cut into smaller subsections focused on specific business areas such as finance or marketing etc.

Access Tools

This refers to business tools like Power BI, Tableau, or Looker, which allow users to run reports, dashboards, and visualizations.

What are the models of Data Warehouse?

There are several different models for building a data warehouse, and the chosen model determines how the data is structured, stored, and accessed. We’ll discuss some of the models often used by organizations.

The first model we’ll go through is the top-down model. Bill Inmon initially proposed this model, and it states that a centralized data warehouse should be created first, from which data marts are built. This model ensures that data is structured, consistent, and overall good for long-term strategy. Its only downside is that it can take a long period of time to implement.

Contrary to this is the bottom-up model, which promotes starting with smaller data marts and then integrating them into a warehouse later. Initially put forward by Ralph Kimball, this model is faster to deliver, but consistency can be an issue.

For organizations that like a bit of both models, they can go with a hybrid setup combining both approaches. This would come in the shape of starting with just the critical data marts while simultaneously building a scalable workhouse.

The fourth and final model we’ll discuss is the federated model. This model is based on the idea that instead of collecting and holding data in a single physical location, multiple databases are integrated virtually. This is good for smaller organizations or startups that have limited funds.

Steps to design a Data Warehouse:

Designing a data warehouse is a complex and tedious process that requires careful planning and execution. Here’s a step-by-step process for designing a successful data warehouse.

1. The first step, as with any process or task, is to clearly define your requirements. This involves understanding what different stakeholders require and the metrics that matter to prevent costly redesigns later. 
2. The next step is to identify the different sources from which information is to be collected, including CRMs, ERPs, and marketing tools. The data contained by each source should be mapped out, along with the frequency of updates, e.g., daily, weekly, or monthly. 
3. Once this is clear, the next plan of action is choosing what technology stack you’re going to use, including the database. There are several different options to choose from, including Snowflake, Amazon Redshift, Google BigQuery, and more. Ultimately, the chosen database and tech stack should be those that ensure scalability, are cost-effective, and offer smooth integration.
4. After the technical part is completed, the next step is related to design, and which schema (blueprint) should be chosen. This will differ for companies as each schema is different and catered to different needs. A star schema is used for simplicity, the snowflake schema is used for normalization, and the galaxy schema is used for complexity. 
5. After the design is agreed upon, the next step involves planning the ETL process by mapping how data flows from multiple data sources to staging, and eventually to the warehouse. 
6. Finally, security is an important part of any hardware, and so robust security controls should be implemented. These include encryption, permissions, and compliance with GDPR or HIPAA standards.

Best practices to follow for Data Warehouse design

These are some of the best data warehouse design practices consistently followed by successful businesses across various industries. They can be the difference between a data warehouse simply being a place that stores data and one that’s a strategic asset.

• You should start small and integrate just the most important metrics first, and then expand from there. Integrating every single data source at once isn’t recommended.
• Data is worthless if it isn’t of good quality or if it’s presented in a way that isn’t useful to the user. A fast, scalable warehouse is useless if the data is inaccurate.
• Cost-effective cloud-based warehouses can be used as an alternative to physical databases. Through this, storage can be scaled more effectively, and computing can be performed independently.
• From the initial planning process to the security controls, everything should be documented and saved for future maintaining.
• Automation is also possible in certain areas and is encouraged. ETL jobs, quality checks, and alerts for any bugs or failed processes can be automated.

Build your Data Warehouse with Coding Crafts

Designing a data warehouse is a tricky process, and it can get even trickier when you’re doing it for the first time or if no one in your team has any prior experience in this. But you don’t have to do it alone. Coding Crafts has helped several businesses of all sizes, from startups to enterprises, build scalable and high-performance data warehouses.

If you’re ready to turn scattered, unprocessed data into a strategic advantage, let’s talk. The sooner you start, the sooner you’ll have answers instead of spreadsheets.