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BI FACT TABLE TIMESLICE HOW TO

We want to compare these two tables month by month.

BI FACT TABLE TIMESLICE HOW TO

To learn how to add a date table to a model, see this webinar post. We start with the same model we had the last post, but now we have added a sales goal table and a Calendar (date) table. This way our date table contains unique date values that can be used to filter the Sales and Sales Goal table across any time frame. Rather than use a messy date relationship between our Sales and Sales Goals table, we should utilize a date table. If we want to add a sales goal table to our model, we need to compare our actual sales to sales goals over different time periods. Let’s look at this last point in an example.Ī common dimension that is shared across multiple fact tables is a date table. If you build your data model correctly from the start you won’t need to fix your measures, columns, visuals later to account for new data. It is difficult to add data from a different source when your data is one table.

Finally, as your report grows you may need to relate tables from different sources.

If you need to change multiple dimension attributes, it’s easier to work on a small dimension table than a large table with millions of rows.

As a Power BI report developer, this makes your job easier as well.

In large datasets a dimensional data model is crucial for an efficient Power BI file we don’t want to repeat data if we don’t have to in our fact table.

Here are three good reasons to use a dimensional data model: You certainly can, but we wouldn’t recommend it. Why do I need to know this? Why not have all my data in one table?

The fact table is tall and narrow (very few fields but many rows) and the dimension table is short and wide (many descriptive fields but very few rows). A dimensional data model is composed of fact and dimension tables. When we arrange a data model to have dimension and fact tables, we often call it a dimensional data model. In the last post, our file had two dimension tables (Manufacturer and Product) and one fact table (Sales). A fact table has mostly numbers that you perform arithmetic functions on, e.g. Think of dimensions tables as qualitative or descriptive data, e.g. We frequently refer to business entities like product or manufacturer as dimension tables, and tables like sales as fact tables.

Let’s start this post by introducing a few more common data modeling terms. Welcome to part 2 of our introduction to data modeling basics in Power BI (you can find part 1 here ).