How can you ensure the scalability of your dimensional model?

1 Choose the right granularity

Granularity, or the level of detail or aggregation of the data in your fact tables, is essential for achieving a balance between query speed, storage space, and data quality. If you choose a too low granularity, you may end up with large and sparse fact tables that take longer to load and query. On the other hand, if you choose a too high granularity, you might miss important details and accuracy of the data. When deciding on the right granularity for your dimensional model, it’s important to consider the business requirements and questions that the users want to answer with the data, the availability and reliability of the source data, the trade-off between storage cost and query performance, as well as whether or not it’s possible to create aggregate or summary tables for faster queries.

2 Use surrogate keys

Surrogate keys are artificial identifiers assigned to rows in your dimension tables. Typically, they are sequential numbers with no meaning or relation to the business data. Utilizing surrogate keys can help ensure the scalability of your dimensional model in a variety of ways. For example, they can simplify the loading and updating of the data, reduce the size and complexity of fact tables, enable the use of slowly changing dimensions, and improve query performance due to being indexed and optimized for joins.

3 Apply partitioning and indexing

Partitioning and indexing are two common methods to improve the performance and scalability of your dimensional model. Partitioning involves dividing a large table into smaller and more manageable chunks, based on criteria such as date, region, or product category. Indexing involves creating and maintaining data structures that allow faster access and retrieval of the data, based on some columns or expressions. Applying these techniques can reduce scan time and IO operations, as only relevant partitions or indexes need to be accessed. Additionally, partitioning and indexing can parallelize loading and querying of the data by distributing the partitions or indexes across multiple nodes or disks. Furthermore, these methods can improve data quality and consistency by enforcing constraints and checks on the partitions or indexes. Finally, they can facilitate archiving and purging of the data by easily dropping or moving old or unused partitions or indexes.

4 Consider using star or snowflake schemas

Star and snowflake schemas are two common types of dimensional schemas that differ in the way they organize the dimension tables. A star schema has a single fact table and multiple dimension tables that are directly connected to the fact table; whereas, a snowflake schema has a single fact table and multiple dimension tables that are further normalized into sub-dimension tables. The choice between star and snowflake schemas can affect the scalability of your dimensional model in various ways. For instance, a star schema can offer faster and simpler queries due to only needing to join the fact table with the dimension tables. Additionally, a snowflake schema can offer lower storage space and higher data quality, as you can eliminate redundant and inconsistent attributes by normalizing them into sub-dimension tables. Furthermore, a star schema can be more flexible and adaptable, as you can easily add or modify the dimension tables without affecting the fact table or sub-dimension tables. Lastly, a snowflake schema can be more scalable and modular, as you can reuse and share sub-dimension tables across multiple fact tables or dimensions.

5 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?