How do you choose the right level of granularity for your fact tables and dimensions?

1 What is granularity?

Granularity refers to the level of detail or aggregation of the data in your fact tables and dimensions. For example, a fact table that records sales transactions at the product, customer, and date level has a higher granularity than a fact table that records sales totals at the product category, region, and month level. A dimension that stores customer names, addresses, and phone numbers has a higher granularity than a dimension that stores customer segments and loyalty tiers.

2 Why does granularity matter?

Granularity matters because it affects the performance, usability, and flexibility of your data warehouse and business intelligence tools. Generally speaking, higher granularity means more detail, more accuracy, and more analytical possibilities, but also more storage space, more processing time, and more complexity. Lower granularity means less detail, less accuracy, and less analytical possibilities, but also less storage space, less processing time, and more simplicity.

3 How to balance the trade-offs?

When deciding on the right level of granularity for your fact tables and dimensions, there is no one-size-fits-all answer. It depends on your business needs, data sources, data quality, data volume, and technical constraints. However, some general guidelines can help you make informed decisions. Start by considering the business questions you want to answer and the metrics you want to measure. This will determine the level of detail and aggregation you need. Additionally, assess the availability and reliability of your data sources, as well as the volume and frequency of your data. Finally, consider the technical capabilities and limitations of your data warehouse and business intelligence tools. You may need to adjust your granularity to fit the requirements or leverage the features of your tools.

4 How to implement granularity in star schema design?

Star schema design is a common approach to dimensional modeling that involves creating a central fact table with measures or facts of a business process, surrounded by dimension tables with attributes or dimensions of business entities. To implement granularity in star schema design, you need to identify the grain or lowest level of detail of the fact table, which will define the primary key and foreign keys, as well as the level of aggregation of measures. Additionally, you must identify the hierarchies or levels of aggregation of the dimension tables, which will define the attributes and relationships, as well as the level of detail of the dimensions. Finally, design and populate the fact and dimension tables according to the grain and hierarchies, which may require ETL processes to extract, transform, and load data from data sources.

5 How to test and validate your granularity choices?

Once you have implemented your granularity choices in your star schema design, it is important to test and validate them to ensure that they meet your business and technical expectations. To do this, you can perform data quality checks using data profiling, data cleansing, and data auditing tools. Additionally, you can use data visualization, data mining, and data modeling tools to perform data analysis and reporting. Lastly, you can use performance monitoring, performance testing, and performance improvement tools to perform performance tuning and optimization.