What are the benefits and drawbacks of using snowflake schema over star schema?

A snowflake schema is an offshoot of the star schema where dimensions are then broken down into further sub-dimensions (i.e. dim_Location > dim_LocationRegion). This allows for further slicing and dicing of data and more powerful reporting and analytics. Some drawbacks are that with more tables, it inherently requires more maintenance.

Snowflake is a data warehouse modeling technique that includes normalizing data into multiple related tables. It has foreign keys to dimension tables and a primary fact table with quantitative data. Transactional data, including sales, costs, and profits, are usually included in the fact table. A snowflake-shaped arrangement of dimension tables surrounds the fact table. Dimension tables include details about the business entities, such as the date, the product, the consumer, and so forth. In this architecture, dimension tables are normalised into several linked tables. As a result, secondary dimensions branch out of fundamental dimensions to provide the optical impression of a snowflake.

Snowflake is a data storage model that helps us on how we organize the data into tables and join them. It is similar to star schema. Here the dimensional tables are connected to through other dimensional tables, there is one fact table, there is no direct connect between sub-dimension table and fact table, it is connected through the main dimension table. For example for sales_order fact table there is a customer_details dimension table, there is a country_dim (country from which the order is placed) is connected to customer_details and not directly to sales_order table here. Due to its complex structure it looks like a snowflake, because of this there is increase in number of joins affecting performance of the data retrieval.

One of the main attributes of Snowflake is it inherently keeps referential data integrity in place (in case of proper primary and foreign keys design). In turn good Snowflake design results in cleaner and more integral datasets.

A snowflake schema is a type of database schema used in data warehousing. It is a more complex version of the star schema where dimension tables are normalized into multiple related tables, forming a snowflake-like structure.

This method of dimensional data modelling is intended for use in BI and data warehousing applications. To link with dimension tables, a core fact table with numerical metrics and foreign keys is present. Dimension tables surround the fact table like Snowflake and denormalized dimension tables have relevant attributes in one location for every table. This reduces joins and accelerates query processing. For instance, a time dimension may combine the columns for year, month, and day into a single table rather than dividing them up into several tables. With radiating dimensions tables all around and a core fact table, the star structure resembles a star. It offers quick aggregation performance for analytical queries and is simple to comprehend.

Today BI tools let's you model Star schemas out of Snowflake source data easily. Old (good?) times of cubes are mostly gone or at least pushed as a legacy.

Star schema is a bottom up Kimball approach. This organizational structure uses single fact table and one or more dimensional tables that is central fact table and multi dimensional table which looks like a star. It requires few joins as the star schema is best suited for simple datasets that makes query data retrieval fast, making it easy to use by organizational users which are the benefits of star schema. On the other hand due it's simple structure there is redundant data and has limited storage space reduces the grains of data.

A star schema is a type of database schema used in data warehousing. It consists of a central fact table connected to dimension tables, forming a star-like structure. The dimension tables are denormalized, meaning they contain redundant data to optimize query performance.

Star Schema: A star schema is a data warehouse schema where a central fact table is connected to several dimension tables, forming a star shape. In a star schema, dimension tables are denormalized, containing redundant data to minimize joins and improve query performance. All dimension tables are directly linked to the central fact table with no intermediate tables. Example: A Sales fact table might be connected directly to Product, Customer, Time, and Store dimension tables.

Normalization: Snowflake does normalisation by dividing dimensions across numerous tables, whereas Star schema employs denormalized dimensional tables. Structure: A Snowflake has a complex structure with multi-level dimensional hierarchies, while a Star has a basic one-level structure. Query Performance: Because Star schema has fewer table joins, queries execute more quickly. Data redundancy: Star schema may have a high level of data redundancy. Snowflake uses normalization to minimize redundancy. Scalability: By splitting vast amounts of dimension data across several tables, Snowflake is more scalable. Analytics: Star performs better in simple aggregate queries. Advanced analytical tasks are better handled by Snowflake.

Snowflake Schema ---> * Normalized * More no. of Joins * Non Redundant (Low storage space) * Makes writes faster * Data Integrity is maintained * Speeds up Insert, Update and Delete operation Star Schema ---> * Denormalized * Less no. of Joins * Redundant (More storage space) * Makes reads faster * Data Integrity may not be retained * Speeds up Select operation

1. Structure: -> Star Schema: Central fact table connected to denormalized dimension tables, resembling a star. -> Snowflake Schema: Central fact table connected to normalized dimension tables, forming a snowflake shape. 2. Query Performance -> Star Schema: Faster queries due to fewer joins. 3. Data Redundancy -> Star Schema: Higher redundancy with repeated data in dimension tables. 4. Storage Efficiency -> Snowflake Schema: More storage-efficient with normalized data. 5. Complexity -> Star Schema: Simpler design, easier to understand and manage. 6. Data Integrity -> Snowflake Schema Higher data integrity with less redundancy.

Choosing between a snowflake schema and a star schema depends on factors like dataset size and complexity. Snowflake is best for large, complex datasets with detailed dimensions and analytical queries. Star schema suits smaller datasets, optimizing query speed for reporting and aggregation. It's about finding the right balance between performance and flexibility based on your specific data needs.

Both Star schema and snowflake schema are data modeling techniques, that refer to the way we organize the data in database, how they are related tables if we want to join them. Both use bottom up Kimball approach. These can be differentiated using normalization and denormalization, structure of the dataset (fact and dimension table). Star schema has single fact table connected to dimension table where as snowflake fact tables have no direct connection to dimension tables. Star schema is best suited for simple datasets that do not require multiple level hierarchy for faster results. Snowflake schema is best suited for complex datasets with detailed structure. Data modeling depends on the business requirements.

Choosing between them depends on your priorities: simplicity and performance (star) versus storage efficiency and data integrity (snowflake).

In simple words, Star Schema safe to use when facts constitute majority of the data And Snowflake Schema safe to use when dimensions are huge in size

Star: - There are comparatively small dimensions of data sets. - Performance of queries is crucial. - Aggregate queries, not advanced?analytics, are the main focus. - The business requirements?are simple and unlikely to alter regularly. - To make maintenance and usage easier, a simple schema design is recommended. Snowflake: - Dimensional data sets should partitioned due to their large size. - More complex analytical searches than merely aggregates are needed. - Business requirements?are intricate and subject to change. - Dimension attributes must be regularly added, deleted, or modified. - Flexibility is required to expand dimensional modelling without having to rebuild the schema. - Redundancy in data must be minimised.

Don't let be drawn into the selection between star or snowflake. Consider common use cases and use model which fits the best. Also think of storage capabilities. SQL servers are very performant even in cases of complex stars especially when data are properly optimized and indexed. NoSQLs and Lakes are on the other hand not that effective in complex joins, but are blazingly fast on denormalized data. However denormalization puts pressure on data (mainly referential) integrity, which is best kept in snowflake. Also, if performance is the key but latency doesn't play that big role, one can create star view out of snowflake. Keep in mind that most modern BI tools lets you model and de-normalize from snowflake easily. No universal rule of thumb.

Snowflake Schema: Higher data integrity due to normalization, reducing data anomalies. Star Schema: Lower data integrity with potential for data anomalies due to denormalization.

Use Snowflake Schema: When data storage efficiency is a priority, and reducing redundancy is crucial. When ensuring high data integrity and consistency is essential. In cases where complex queries and advanced analytics are less frequent. Use Star Schema: When query performance and simplicity are critical. In environments with high query volume and need for fast response times. When ease of maintenance and straightforward data modeling are important.