Why it is important to use Snowflake schema

 Choosing the right schema generates valuable data insights. Choosing the wrong schema generates incorrect and misleading insights. How do you select a schema? In this video, you will discover why the Snowflake schema is often the most suitable schema for your datasets. Adventure Works wants to use its data to generate business insights into its sales and marketing practices. It needs to structure its data in a way that enables efficient querying and analysis. It considers using a star schema. However, the last star schema it used resulted in an overly simplified and denormalized dataset. You suggest a Snowflake schema to more accurately represent and analyze the complex relationships between its data components. As you discovered in earlier lessons, a star schema organizes data into a central fact table. This central fact table is surrounded by dimension tables containing descriptive attributes. This structure is suitable for certain kinds of analysis. For example, it's useful for analyzing smaller datasets. However, it becomes problematic when dealing with more complex hierarchical relationships. This is particularly true for the Adventure Works dataset. By using the star schemas denormalized approach, Adventure Works risks generating results that contain redundant data and a loss of data integrity. This would make it difficult to perform an accurate analysis of the data. On the other hand, a Snowflake schema would provide a much better approach. As you discovered previously, the Snowflake schema optimizes data storage and retrieval by normalizing the data into multiple related tables. This structure provides more flexibility in defining complex dimension hierarchies and it allows for the creation of sub-dimensions within these hierarchies. This lets analyst explore data at much deeper levels of granularity. However, the downside is that increased table sizes result in slower query performance. This impacts the team's ability to derive insights and make data-driven decisions quickly. The best approach for Adventure Works is to build a Snowflake schema. This schema uses a more normalized approach, which is more beneficial for dealing with intricate data relationships. It can be used to build out multiple levels of related tables in the form of a hierarchy. This is much more efficient than a star schema, which flattens a hierarchy into a single table. You can normalize several of the tables in the Adventure Works dataset. For example, the product dimension table can be split into two separate tables, category and subcategory. This structure makes it much easier to analyze the performance of individual products and their related categories through deeper granularity. Customer data can also be organized in a hierarchy. The team can explore customers and their purchases by country, state, and city. This level of granularity reveals insights into regional sales patterns and marketing campaigns. Another benefit of this hierarchical structure is that it helps the team to identify patterns and relationships between data sets. A Snowflake schema also eliminates data redundancy. Each attribute is stored only once in its respective table, and a unique identifier ensures consistent and accurate data. Finally, the normalization of dimension tables also helps to reduce the data model storage requirements. This makes the Snowflake schema a much more efficient approach. Choosing the right schema is crucial for data analysis, especially when dealing with complex datasets. As the case of Adventure Works shows, opting for a Snowflake schema can help avoid the risks of using a star schema for hierarchical data relationships. As an entry-level data analyst, understanding the importance of using the correct schema for your data set is crucial. By recognizing when a Snowflake schema is more appropriate than a star schema, you can optimize your data analysis process, leading to more accurate insights and better-informed decision-making.