What are some common dimensional modeling patterns and how do you apply them to different scenarios?

In my opinion, the star schema is a good options for simple and specific business processes with statict context data that require to analyze or consuming data in the data marts by business teams. It can use to build a data warehouse component: it will accelerate the delivery if the component and development time; but it can't resolve more business analytics cases than chosen by the processes.

While star schemas are straightforward and facilitate fast query performance due to their denormalized nature, consider using them in environments where query performance is a critical success factor, such as real-time dashboards and reporting tools. Additionally, the simplicity of star schemas makes them ideal for end-users who interact directly with the data, enabling them to construct queries without needing deep technical expertise. However, beware of scenarios where the volume of data becomes overwhelming, as maintaining data quality across denormalized tables can become challenging.

- ?? Use a snowflake schema to normalize dimension tables into sub-dimensions, creating a more complex but organized structure. - ?? Apply it in scenarios with dynamic data or varying granularity in fact tables to handle complex or hierarchical dimensions. - ?? Reduce data redundancy and improve data quality by breaking down dimensions into more detailed tables. - ?? Support multiple levels of aggregation, providing flexibility in data analysis and reporting. - ?? Be cautious of increased query complexity and potential performance impacts due to more joins and tables involved.

Based in the star schema; the main benefict in snowflake schema is the capacity to handle changes in dimension tables when it changes: reduce all more rows to add or apply fix on value in row easier.

Snowflake schemas are particularly effective when dealing with highly volatile data that requires frequent updates, as the normalized structure can make updating easier and reduce anomalies. Use snowflake schemas when the need for referential integrity is paramount, and when maintaining a high degree of data quality is more important than the complexity of queries. However, balance this with the additional computational overhead and complexity in query optimization that snowflake schemas introduce.

Junk dimensions work well when there are numerous miscellaneous attributes that would otherwise clutter the fact table. By aggregating these attributes into a single table, you can simplify the data model. To maximize the effectiveness of junk dimensions, ensure that the attributes combined have logical coherence, reducing the risk of ambiguity. Carefully monitor the growth of the junk dimension to prevent it from becoming too large, which could offset its performance benefits.

One of the objectives for the design and development of data warehouses is to maintain the historical data. The Dimensional tables are expected to support the concept of Slowly Changing Dimensions to support that. SCDs are achieved through types I, II and III. Time Dimension is one of the common dimensions that could be found with data warehouses built upon Ralph Kimball's Dimensional modeling. Factless Fact tables can also be considered as common pattern in dimensional modeling.