What's the best way to choose a data warehouse architecture for your project?

2 Choose a data warehouse model

The next step is to choose a data warehouse model that suits your data sources and requirements. A data warehouse model is a logical representation of how data is organized, structured, and related in a data warehouse system. Two main types of models exist: the dimensional model and the relational model. The dimensional model uses a star or snowflake schema to organize facts and dimensions. Facts are numerical measures of business events or transactions, while dimensions are descriptive attributes that provide context and analysis. On the other hand, the relational model uses a normalized schema to organize data into tables and columns with primary and foreign keys. This model is simple, consistent, and efficient for data storage and integration; however, it may require more complex and slower queries for analytical purposes. Depending on your data sources and requirements, you may choose one or a combination of these models, or use other variations or hybrid approaches.

3 Select a data warehouse architecture style

The third step is to select a data warehouse architecture style that matches your data warehouse model and your data sources and requirements. A data warehouse architecture style is a physical representation of how data is stored, processed, and accessed in a data warehouse system. The traditional architecture uses a single, centralized data warehouse that stores all the data from different sources, after applying the extract, transform, and load (ETL) process. This architecture is simple and reliable but may not scale well with large or complex data sets. Alternatively, the distributed architecture uses multiple, independent data warehouses that store subsets of data from different sources. This architecture is scalable and flexible but may have data quality and consistency issues. Lastly, the cloud-based architecture leverages the cloud infrastructure and capabilities to store, process, and access data in a data warehouse system. This architecture is cost-effective but may have data privacy and compliance risks. Depending on your needs, you may choose one or a combination of these styles or use other alternatives or emerging technologies.

4 Evaluate the data warehouse performance and scalability

Evaluating the data warehouse performance and scalability is the fourth step in the data warehouse architecture process. This is essential for ensuring the efficiency, quality, and value of your data analytics and business intelligence solutions. To evaluate the data warehouse performance and scalability, you should consider its capacity, speed, reliability, and flexibility. For example, how much data can it store and process? How fast can it load, update, and query data? How stable and secure is it? And how adaptable is it to new or changing data sources and requirements? Answering these questions will help you measure and optimize the data warehouse performance and scalability, as well as identify any potential bottlenecks.

5 Compare the data warehouse costs and benefits

The final step in data warehouse architecture is to compare the costs and benefits. This is essential for determining the feasibility, profitability, and return on investment of your project. To compare these two aspects, you should consider the development costs, such as the time, money, and resources needed to design, build, and test your data warehouse system. You should also consider the operational costs of running, maintaining, and upgrading your data warehouse system. Lastly, you should consider the business benefits of your data warehouse system and how much value, insight, and advantage it can bring. By taking these factors into account, you can balance and justify the data warehouse costs and benefits to decide on the best architecture for your project.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?