More than occasionally, I work in data warehouse environments where there have been very loose standards when it comes to creating physical data models. One example of this is delivering dimensions and facts without surrogate keys, and delivering ‘attributes’ directly into fact tables. I believe data architects and engineers do this because it is easier, and they think it doesn’t really matter.
But it does.
Using surrogate keys that are integers in a dimensional model in a data warehouse offers several advantages:
- Uniqueness and Consistency: Surrogate keys are guaranteed to be unique across the entire data set, ensuring that each record can be uniquely identified. This is critical for maintaining data integrity, especially when dealing with large and complex data sets where natural keys might not be unique or could change over time.
- Performance: Integer keys are more efficient for database operations. They consume less space and are faster to index and search compared to string-based natural keys. This efficiency gain is particularly noticeable in joins, which are common operations in data warehousing. If you are using Snowflake, better performance costs less. Weird, huh?!?
- Handling Changes in Source Data: Natural keys can change, and when they do, it can complicate the data model and historical data tracking. Surrogate keys remain stable even when the source system changes its natural keys, which helps in maintaining historical accuracy and consistency.
- Simplification of ETL Processes: Using surrogate keys simplifies Extract, Transform, Load (ETL or ELT) processes. Surrogate keys allow for easier handling of slowly changing dimensions (SCDs) by providing a stable, unique identifier that does not change with source data updates.
- Decoupling from Source Systems: Surrogate keys decouple the data warehouse schema from the source systems. This provides flexibility to modify or integrate new source systems without affecting the warehouse schema. If you need to match something between systems, like customers in SalesForce and Oracle EBS, for example, this might be the only reasonable way to do it.
- Storage Efficiency: Integer keys typically use less storage space compared to string-based natural keys. This can lead to significant storage savings, especially in large data warehouses with billions of records.
- Improved Query Performance: Query performance is generally better with integer keys. Since they are smaller in size and more efficient for the database engine to process, queries involving joins and lookups run faster.
- Data Consistency: Surrogate keys ensure data consistency across different tables. In scenarios where different source systems might use different natural keys for the same entities, surrogate keys provide a single, consistent identifier within the data warehouse.
- Referential integrity. This is fundamental data governance. The act of constructing a dimension from one source where you expect the universe of values, and then process data from another source using it as a lookup to replace keys ensures referential integrity. You can: reject records, accept them and add them to the lookup, reject and replace with ‘NA’, but you will discover and correct data problems in the process.
- Optimize for downstream use: Replacing complex keys and lookups with a single integer key makes the data more portable and deployment more flexible. Power BI (for example) needs one key and it does not support all data types when defining joins Assigning and using integer surrogate keys works universally for downstream operations.
Overall, the use of integer surrogate keys in dimensional modeling provides a robust, efficient, and scalable solution for managing and querying large and complex data sets in a data warehouse environment. It does not take much effort to implement them and the it is always worth it..
Let me know if you have anything you’d add.
elealos@quantifiedmechanix.com
