One cannot create a Power BI report without an appropriate data model any more than a person can create a house without a blueprint. Everything may be okay on the outside, but when you gain weight, things will start bustling.
You should be able to master the art of the schema in order to ensure that your reports are fast, accurate, and modular. The Power BI Pro data modeling is destined here.
1. Fact vs. Dimension Table:
This is why in any data model the first thing that you should have is categorization of your tables. Suppose we are isolating the verbs and the nouns.
Fact Tables (The Verbs): It is information of fact—the events of the business. It may be transferred during sales, stock transfer or temperature. They are very high (millions of lines) and too narrow (not so many columns).
Dimension Tables (The Nouns): They will provide the outlay of the context. Who bought the product? Where was the store? What was the date? Filters, slices, and grouping dimensions are used.
2. The Gold Standard: Star Schema.
The last objective of Power BI is the star schema. It consists of a central fact table and a collection of dimension tables, which are joined to the central fact table using one-to-many (1:) relationships.
Why it’s the best choice:
Performance The horizontally distributed Power BI engine (VertiPaq) is most favorable to drive through the space of sparse fact tables and stratify them by dimension.
Simplicity: It is also simple to the data analysis expressions (DAX) because the relationship paths are simple as well.
Usability The end-user also finds it easy to move around due to the ease with which the fields radiate out of the center of interest.
3. Another Alternative that Might Have Occurred: Snowflake Schema.
An eventuality that occurs through the normalization of your dimension tables, i.e., splitting them into sub-dimensions, is known as a snowflake schema. You have no product table; you are a product subcategory category.
The Issue: This will save part of the storage (redundancy will be minimized), and it will require Power BI to make numerous joins so as to get a simple solution.
The Conclusion: Power BI is more preferable as far as Flat is concerned. You need to do what you can, and then you need to shred your snowflake branches into a one-dimensional table (one dimension, no cross-dimension) (which is known as denormalization).
4. The Relationships: The definition of the pipes.
Relationships are the logic gates that facilitate the data movement between the tables. To maintain your model in good condition, you have to observe the following 3 rules:
Cardinality: One-to-Many (1:1). Even when a relationship is not an alternative, many-to-many relationships should not be recommended since this may end up slowing down performance and misunderstanding of the outcome.
Direction of Cross-Filter: This is to be single (dimension to fact filtering). Both or bidirectional filtering should be done with a lot of care, as this can be one of the reasons for the circular dependencies and logic errors.
The Date Table: Power BI auto date/time forgotten. To ensure the correctness of the time-intelligence functions, it is necessary to make a special table date dimension.
5. The significance of good modeling.
In the long run, it will not be capable of modeling a report that is not modeled appropriately. The time that was devoted to the schema is due to the following reason:
Precision: It is possible to count the same numbers once or to represent the same number in each row unless they are interconnected in the appropriate way. Correct modeling will ensure that 1 + 1 = 2.
Speed: In an A-star schema, the engine can jump into unwanted data, i.e., your dashboards are displayed in milliseconds and not seconds.
Maintenance: A clean schema is a plug-and-play system in a case where your business is expanding and therefore requires additional data. A spaghetti model (also called a messy model or a flat file) cannot even make a single step of transforming a single variable.
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