Fact tables are an essential component of a data warehouse. They are used to store the quantitative data that is the focus of analysis and reporting. In this article, we will explore the concept of fact tables and discuss some important considerations when populating them.

What is a Fact Table?

A fact table is a table in a data warehouse that contains the measurements or metrics of a business process. It typically consists of foreign keys that reference the dimension tables and the measures that quantify the business process. For example, in a sales data warehouse, the fact table would contain foreign keys to the date, product, and customer dimensions, as well as measures such as quantity sold and revenue.

Populating Fact Tables

When populating a fact table, the natural keys from the source system need to be converted into surrogate keys. This is done by joining the fact table with the dimension tables on the natural keys and retrieving the corresponding surrogate keys. It is important to note that all dimension tables must be populated before populating the fact table to ensure that the surrogate keys are available.

Let’s consider a simplified example. We have an order detail table with columns such as order_id, order_date, product_id, quantity, and price. In our data warehouse, we have a star schema consisting of a date dimension, a product dimension, and a sales fact table. The fact table contains columns such as date_key, product_key, order_id, quantity, price, and load_time.

To populate the fact table, we would join the order detail table with the dimension tables on the natural keys and retrieve the corresponding surrogate keys. We would then insert the records into the fact table, ensuring that the load_time column is populated with the current load time.

Considerations for Fact Tables

There are several important considerations when working with fact tables:

1. Loading Partitioned Fact Tables

Partitioning a fact table can improve query performance and increase availability. It involves dividing the fact table into several physical parts called partitions. Each partition can be loaded separately in parallel, resulting in faster loading times. Partitioning can be particularly beneficial for large fact tables with millions of rows.

2. Slim Lookup Tables

When a dimension is very large, it may be beneficial to create a separate lookup table for key management. This slim lookup table would contain only the necessary columns for dimensional key lookup, improving performance and suitability for in-memory lookup.

3. Deduplication

Deduplication is the process of removing duplicate records from a fact table. This can occur when loading records from a stage table or when loading data from multiple source systems. It is important to define the business rules for handling duplicates and implement them in the loading process.

4. Loading Snapshot and Delta Fact Tables

Snapshot fact tables capture the status of a business process at a specific point in time, while delta fact tables capture the changes between two time points. Loading these types of fact tables requires comparing the condition of the source table at different time points and updating the fact table accordingly.

5. Dealing with Fact Table Indexes

Fact tables can be very large, so indexing them can improve query performance. It is important to carefully consider which columns to index based on how the fact table will be used. Dropping and recreating indexes may be necessary for efficient loading, especially for large fact tables.

Conclusion

Understanding and effectively populating fact tables is crucial for building a successful data warehouse. By following the basic steps and considering important factors such as partitioning, slim lookup tables, deduplication, snapshot and delta fact tables, and fact table indexes, you can ensure the accuracy and efficiency of your data warehouse.

References:

1. Populating Fact Tables (Data Warehouse Loading Part 3) by Vincent Rainardi