Example of Converting ERD to Star Schema
Example of Converting ERD to Star Schema. Step 1. Separate the master ERD into separate business processes or units. Below is Then designate fact tables. PDF | Star schema, which maintains one-to-many relationships between dimensions and The fact table contains thousands, or even millions of rows . connect multiple diagnoses to a fact table [KRRT98]. relationship between two entities. They also are found in join operations that combine fact and dimension tables. We will look at three anti-patterns in data warehouse (DWH) modeling between . in some business sense, between multiple parent entities in the dimensional table. If there are a limited or fixed number of many-to-many relationships in the .Difference between ER Modeling and Dimensional Modeling
The granulation inside each dimension is also determined by our reporting needs. The Snowflake Schema This snowflake schema stores exactly the same data as the star schema.
The fact table has the same dimensions as it does in the star schema example. The most important difference is that the dimension tables in the snowflake schema are normalized. Interestingly, the process of normalizing dimension tables is called snowflaking.
Once again, visually the snowflake schema reminds us of its namesake, with several layers of dimension tables creating an irregular snowflake-like shape.
Normalization As mentioned, normalization is a key difference between star and snowflake schemas.
Regarding this, there are a couple of things to know: Snowflake schemas will use less space to store dimension tables. This is because as a rule any normalized database produces far fewer redundant records.
Five Common Dimensional Modeling Mistakes And How to Solve Them
Denormalized data models increase the chances of data integrity problems. These issues will complicate future modifications and maintenance as well.
To experienced data modelers, the snowflake schema seems more logically organized than the star schema. This is my personal opinion, not a hard fact. Query Complexity In our first two articles, we demonstrated a query that could be used on the sales model to get the quantity of all phone-type products sold in Berlin stores in The star schema query looks like this: Because the dimension tables are normalized, we need to dig deeper to get the name of the product type and the city.
We have to add another JOIN for every new level inside the same dimension. In the star schema, we only join the fact table with those dimension tables we need.
Modifying records is generally known as online transaction processing OLTP. Data retrieval is referred to as online analytical processing OLAP or decision support, because the information is often used to make business decisions. This section describes these data models and their structural requirements.
When database records are modified, the most important requirements are update performance and data integrity. These needs are addressed by the entity relation model of organizing data. Entity relation schemas are highly normalized. This means that data redundancy is eliminated by separating the data into multiple tables. The process of normalization results in a complex schema with many tables and join paths. How to Resolve Roll-Up Incompleteness As roll-up and drill-down incompleteness are mirror images of each other, so are their solutions.
We need to add a default value for the child entity to the parent entity, with a connection between them. In this example, it would be an unallocated category value. Non-Strict Dimension Relationships This is another issue that we can easily identify.
Unlike roll-up and drill-down incompleteness, a non-strict dimensional relationship problem is pure design error. Watch for it when there are many-to-many relationships in the model: The Vertabelo modeling tool does not allow many-to-many relationships.
A good rule of thumb in dimensional modeling and modeling in general is to avoid many-to-many relationships. In this case, I will display the model with two one-to-many relationships. We have a two dimensional tables: The relationship is many-to-many because each month can have many weeks and one week can be in two months.
Non-strict Incompleteness We represent the weeks of a year as a sequence of numbers and months in a year as a list of names. The sum of data of sales in weeks is different than the sum of sales in months because there is an overlap in some weeks.
This happens when a week falls into two months or when certain months are not in our data period March. The data displayed is correct, but it is not roll-upable.
How to Resolve Non-Strict Dimension Relationships We solve this error by placing the dimensions in different hierarchies. If we look at the model for this solution, we see two hierarchies which are independent of one and other. We mitigated the roll-up operation from week to month. For example, in the above case we would define one major category — the major parent — out of many categories.
Dimension-Fact Summarizability Problems Dimension-fact summarizability problems are found in operations between fact and dimensional tables. Like dimensional summarizability problems, they are evident in the erroneous cardinalities of summarized data. When looking at dimension-fact summarizability problems, we commonly see two modeling anti-patterns.
The first relates to the joining of incomplete dimensional table data for all fact table values. The second relates to a non-strict relationship between values in fact and dimensional tables.
Understanding Star Schemas
Incomplete Dimension-Fact Relationships Incomplete dimension-fact relationship problems manifest themselves in join operations between fact and dimensional tables. They occur when the fact table contains measures with no corresponding value in the dimensional table. Summary calculations on the fact table vary depending on the dimensional tables we are using for our calculation. You may have already noticed the incomplete relation to the customer table.
As with the incomplete dimensional model, therein lies the problem. In the first scenario, we must display the sum of all balances for customers on a monthly grain. In the second scenario, we must display the complete sum of all account balances on a monthly grain. As some customers in our fact table are not connected to the customer dimension table i.