What are the best ways to manage large dimensions in a snowflake schema?

1 Normalize large dimensions

One way to manage large dimensions is to normalize them into multiple sub-dimensions that are linked by foreign keys. This reduces the size and redundancy of the dimension tables, and makes them easier to update and join. For example, if you have a customer dimension that has many attributes, such as name, address, phone, email, loyalty status, and preferences, you can split it into separate tables for customer, address, loyalty, and preference. Then, you can join them with the fact table using the customer key.

2 Use surrogate keys

Another way to manage large dimensions is to use surrogate keys instead of natural keys. Surrogate keys are artificial identifiers that are generated by the data warehouse, and have no meaning or relation to the source data. Natural keys are identifiers that are derived from the source data, and have some meaning or relation to the business domain. Surrogate keys are preferable for large dimensions because they are usually shorter, simpler, and more consistent than natural keys, and they avoid the problems of changing or duplicate values. For example, if you use a customer ID as a surrogate key, you don't have to worry about changing names or emails.

3 Partition large dimensions

A third way to manage large dimensions is to partition them into smaller and more manageable chunks. Partitioning is a technique that divides a table into multiple physical segments that can be stored and accessed separately. Partitioning can improve the performance and scalability of the snowflake schema, by reducing the amount of data that needs to be scanned, loaded, or updated. For example, if you have a date dimension that spans many years, you can partition it by month or quarter, and only query the relevant partitions.

4 Use bitmap indexes

A fourth way to manage large dimensions is to use bitmap indexes instead of conventional indexes. Bitmap indexes are a type of index that use bitmaps, or arrays of bits, to represent the presence or absence of a value in a column. Bitmap indexes are very efficient for large dimensions that have low cardinality, or few distinct values, and high selectivity, or high percentage of rows that match a condition. Bitmap indexes can speed up the queries that involve multiple dimensions, by performing fast bitwise operations on the bitmaps. For example, if you have a product dimension that has a category column with few values, such as electronics, books, or clothing, you can use a bitmap index to filter and join the products.

5 Use materialized views

A fifth way to manage large dimensions is to use materialized views instead of regular views. Materialized views are pre-computed and stored results of a query, that can be refreshed periodically or on demand. Materialized views can improve the performance and simplicity of the snowflake schema, by avoiding the repeated computation and joining of large dimensions. Materialized views can also provide aggregated or summarized data that can answer common or frequent queries. For example, if you have a sales fact table that joins with many large dimensions, such as customer, product, date, and location, you can create a materialized view that calculates the total sales by customer segment, product category, month, and region.

6 Use caching techniques

A sixth way to manage large dimensions is to use caching techniques that store the frequently accessed or recently used data in a faster and more accessible layer. Caching can reduce the latency and workload of the snowflake schema, by minimizing the need to query or join the large dimensions. Caching can be implemented at different levels, such as the application, the database, or the query. For example, if you have a dashboard that displays the latest sales trends by product and region, you can use a query cache that stores the results of the queries that generate the dashboard, and refreshes them at regular intervals.

7 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?