Jeson Clarke

What are Slowly Changing Dimensions?

Keeping track of data attributes that change regularly is pretty straightforward. But what if you have data that evolves slowly over time. Measuring that change is still essential for reporting historical data. If you want to understand a slowly changing dimensions approach, visit this website.

Luckily, you can use an appropriate slowly changing dimensions approach to overcome the many challenges of this unique problem.

The Basics of Slowly Changing Dimensions

Slowly changing dimensions (SCD) are attributes that look like standard dimensional data. However, it changes very slowly over time. One example is company size. The number of employees can fluctuate, but it's rarely a regular thing.

The problem with SCD is that it can make your data models diverge from the source of truth, leading to all kinds of accuracy issues.

Using a Slowly Changing Dimensions Approach

There are a few ways to deal with SCD. We won't get into the nitty-gritty details, but here's some high-level information to help you understand how data modelers tackle this issue.

Passive Method

With this method, you don't take action at all. Some dimensional data stays the same, but you can overwrite others to overcome divergence problems.

Overwriting Old Values

This approach is pretty simple. Instead of keeping the history of dimensional changes, you overwrite old data with new data.

Creating Additional Records

This approach involves creating new records of dimensional changes in the database. It can be an expensive operation, but keeping precise records may be worth it.

Adding a New Column

Adding a new column of data can help you store historical data. On one column, you keep the current dimensional data. On the other, you hold the old data.

Using a Historical Table

This technique involves using a separate table. In it, you store historical data about the dimension's changes.

Combination Approaches

Finally, we have the combination approach. This technique involves using a mix of approaches one, two, and three.

Overcoming SCD

Slowly changing dimensions can be a handful. These techniques require some extra work, but they're flexible enough to work in most applications. Most importantly, they're crucial for keeping your data models accurate and up to date.

Author Resource:-

Jeson Clarke writes about technologies, import/export data and customs data tools. You can find his thoughts at self-service analytics blog.