How can you use dimensional modeling for data warehousing?

1 What are dimensions and facts?

Dimensions are the descriptive attributes of the data, such as time, location, product, customer, or salesperson. They provide the context and meaning for the data analysis. Facts are the numerical measures of the data, such as sales amount, quantity, profit, or cost. They represent the performance or behavior of the business processes. Facts are usually stored in tables that are linked to the dimension tables by foreign keys.

2 What are the benefits of dimensional modeling?

Dimensional modeling offers several advantages over other data modeling techniques, such as simplifying the data warehouse structure and improving query performance. It also enhances data quality and consistency by enforcing conformance and standardization. Furthermore, it supports business requirements and expectations by aligning the data warehouse with the business logic and terminology.

3 How to design a dimensional model?

Designing a dimensional model involves several steps. Firstly, you must identify the business process and the grain of the data. The business process is the main subject of the analysis, such as sales, orders, or inventory, while the grain is the level of detail of the data, such as daily, monthly, or per transaction. Secondly, you must identify the facts and dimensions of the data. The facts are numerical measures that correspond to the business process and grain, while dimensions are descriptive attributes that provide context and filters for the facts. Thirdly, normalize the dimensions and denormalize the facts. This means splitting dimensions into separate tables based on their hierarchy and relationship; for example, date, month, quarter, and year. Denormalizing facts involves combining them into one table with all foreign keys to dimensions; for example, sales fact table with date key, product key, customer key, and sales amount. Finally, apply dimensional modeling patterns and best practices to your model. Different types of dimensional models exist such as star schema, snowflake schema or galaxy schema depending on complexity and number of dimensions/facts. There are also various techniques to handle common challenges in dimensional modeling such as slowly changing dimensions, degenerate dimensions or bridge tables.

4 How to implement a dimensional model?

The implementation of a dimensional model depends on the data warehouse architecture and the tools and technologies used, but generally includes ETL, creating physical tables and indexes, and defining metadata and business rules. ETL is the process of extracting data from different sources, such as databases, files, or web services, transforming it to match the dimensional model, such as cleaning, filtering, or aggregating, and loading it to the data warehouse. Physical tables should follow the logical design of the dimensional model and indexes should be used to improve query performance. Metadata is the information about the data, such as name, description, type, or format of dimensions and facts. Business rules are logic and calculations that apply to the data, such as conversion rates, formulas, or filters.

5 How to use a dimensional model for data analysis?

The dimensional model is the foundation for data analysis and reporting in the data warehouse. This can be done through a variety of tools and methods, such as query languages like SQL, business intelligence tools like Power BI, Tableau, or Qlik, and data mining/machine learning techniques like clustering, classification, or regression. All of these methods allow for retrieving and manipulating data from the warehouse while also providing graphical and interactive interfaces to visualize and explore the data. Additionally, they can help discover patterns and insights from the data in the warehouse.

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?