Recommended Best Practices for Data Modeling

Correct Answers: B, C and E

The below image describes the best practices that can be considered while creating data semantic models in, Azure Analysis Services, SQL Server Analysis Services, or Power BI:

Option A is incorrect.

It is recommended to create a dimension model even if you need to ingest data from various sources.

Option B is correct.

You need to create a dimension model snowflake or/and star, even if you need to ingest data from various sources.

Option C is correct.

The best practices ask to only include the integer surrogate keys or value encoding in the model and exclude all the natural keys from the dimension tables.

Option D is incorrect.

The best practices ask to only include the integer surrogate keys or value encoding in the model and exclude all the natural keys from the dimension tables.

Option E is correct.

You should decrease the cardinality to reduce the uniqueness of the values and allow much better compression.

Option F is incorrect.

Decreasing the cardinality will help in reducing the uniqueness of the values and allowing much better compression.

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When creating data semantic models in SQL Server Analysis Services, it is important to follow best practices for data modeling in order to optimize the performance and efficiency of the model. Here are three recommended best practices for data modeling in SQL Server Analysis Services:

1. Create a dimension model in either a snowflake or star schema, even if you need to ingest data from various sources.

The star schema is a popular data modeling technique that consists of one fact table surrounded by multiple dimension tables, with each dimension table connected to the fact table through a foreign key. This approach simplifies the data model and makes queries faster, as there are fewer joins required to retrieve data. The snowflake schema is a variation of the star schema where the dimension tables are normalized to reduce data redundancy. While this normalization can make the model more complex, it can also improve performance and reduce the amount of storage required.

2. Include integer surrogate keys or value encoding in the model and exclude natural keys from the dimension tables.

A surrogate key is an artificially generated key that is used to uniquely identify a row in a table. This key is used instead of a natural key, which is a value that is derived from the data being modeled. Surrogate keys are often preferred in dimensional modeling because they are more stable than natural keys and can improve query performance. Value encoding involves mapping textual or categorical values to integers, which can also improve performance and reduce storage requirements.

3. Increase the cardinality to reduce the uniqueness of the values and allow better compression.

Cardinality refers to the number of unique values in a column. Increasing the cardinality reduces the number of repeated values in a column, which can improve compression and reduce the amount of storage required. This is because compression algorithms can take advantage of patterns in the data, such as repeating values, to reduce the size of the stored data. However, it is important to balance cardinality with performance considerations, as higher cardinality can also increase query complexity and slow down performance.

In contrast, the following practices are not considered best practices for data modeling in SQL Server Analysis Services:

1. Never create a dimension model in both snowflake and star schema when ingesting data from various sources.

This recommendation is not accurate, as both snowflake and star schema can be used effectively in dimensional modeling, even when ingesting data from multiple sources. The choice of schema should be based on the specific needs of the data being modeled and the performance requirements of the queries being run.

2. Only include natural keys in the model and exclude integer surrogate keys or value encoding.

While natural keys can be useful in some cases, they are often less stable and more complex than surrogate keys. Using surrogate keys and value encoding can improve performance, reduce storage requirements, and simplify the data model. Natural keys should only be included in the model if they are necessary for the specific requirements of the data being modeled.

3. Decrease cardinality to reduce the uniqueness of the values and allow better compression.

While reducing cardinality can improve compression, it can also reduce query performance and make the model more complex. It is important to balance cardinality with performance and complexity considerations when designing the data model.