Can the grain of a fact table be changed?

Can the grain of a fact table be changed?

You can do this in a few steps: like this if memory serves: Your market data should be presented to the cube as a view. If it is not create one and in the DSV replace the table with “another table” and point it to the new view.

What is the grain of the fact table?

The grain of a fact table represents the most atomic level by which the facts may be defined. The grain of a sales fact table might be stated as “sales volume by day by product by store”. Each record in this fact table is therefore uniquely defined by a day, product and store.

How do you find the grain of a fact table?

The GRAIN or GRANULARITY of the fact table refers to the level of detail of each row in the fact table. For example, an order fact table might have a grain of order, with one row per order, or order line, with a row for every line on each order (meaning more than one line for some orders).

When we prepare a data warehouse model we need to identify the grain for the model what is grain in this context and provide 1 example each for a grain that is low and a grain that is high?

When you identify the grain, you specify exactly what a fact table record contains. The grain conveys the level of detail that is associated with the fact table measurements. When you identify the grain, you also decide on the level of detail you want to make available in the dimensional model.

Why is going against the granularity of a table bad?

This means that with this grain in place, you can’t analyze inter-day patterns, or which checkout was used, or which shelf the product was on. Makes sense, you have to stop somewhere.

Can a fact table contain duplicate records?

If that is not the case and there are in fact duplicates in the Source, depending on the situation the code can either produce a run-time error or can load duplicate rows into the Base fact table.

What is grain in data modeling?

The grain of the dimensional model is the finest level of detail that is implied when the fact and dimension tables are joined. For example, the granularity of a dimensional model that consists of the dimensions Date, Store, and Product is product sold in store by day.

What is a grain in data?

In the world of data warehousing, the grain of a fact table defines the level of detail that is stored, and which dimensions are included make up this grain. Obviously, the higher the grain the better- although source systems and data volume/performance may intervene.

Can a star schema have multiple fact tables?

Although the diagram in this chapter shows a single fact table, a star schema can have multiple fact tables. A more complex schema with multiple fact tables is useful when you need to keep separate sets of measurements that share a common set of dimension tables.

Why do we create dimension tables?

Dimension tables are used to describe dimensions; they contain dimension keys, values and attributes. For example, the time dimension would contain every hour, day, week, month, quarter and year that has occurred since you started your business operations.

How is the grain of a fact table determined?

The grain of a fact table is the business definition of the measurement event that creates a fact record. The grain is exclusively determined by the physical realities of the source of the data. All grain definitions should start at the lowest, most atomic grain and should describe the physical process that collects the data.

How are fact tables used in dimensional modeling?

Transactional fact tables are the most common fact in dimensional modeling. Transactional fact tables capture the measurement at its most atomic dimensional level, at the point-in-time of the transaction. This allows the fact table to provide robust dimensional grouping and roll-up and drill-down reporting capabilities to the business user.

How to keep to the grain in dimensional modeling?

When developing fact tables, aggregated data is NOT the place to start. To avoid “mixed granularity” woes including bad and overlapping data, stick to rich, expressive, atomic-level data that’s closely connected to the original source and collection process. The power of a dimensional model comes from a careful adherence to “the grain.”

Which is the most common fact in a fact table?

The three basic fact table grains are the transactional, the periodic snapshot and the accumulating snapshot. Transactional fact tables are the most common fact in dimensional modeling. Transactional fact tables capture the measurement at its most atomic dimensional level, at the point-in-time of the transaction.