In Snowflake schema, the fact table is connected to several normalized dimension tables, and these dimension tables have child tables. Users of a snowflake schema benefit from its low levels of data redundancy, but it comes at a cost to query performance.
However, today, both the star schema and the kuwait whatsapp number data snowflake schema are not very relevant due to some fundamental shifts happening in the world of data warehousing.
The star and snowflake schemas were relevant when storage and compute were expensive and limited. Today, with the advent of cloud computing, cloud data warehouses (such as Amazon Redshift, Google BigQuery, Microsoft Azure Synapse, Snowflake, Databricks, and more) offer cheaper, unlimited, and faster storage/compute.
The star and snowflake schemas were relevant when business changes were relatively static. We live in a VUCA (volatility, uncertainty, complexity, and ambiguity) world where companies need the ability to forecast with greater speed, accuracy, and efficiency. Building data models on the star schema and the snowflake schema is time-consuming. Gartner reported that 50% of BI projects will not meet business expectations at the time of going live. While 98% of BI projects are declared successful in week No. 1 after going live, only 50% remain successful by week #10 [2]. So, businesses need data warehouse models that are scalable, flexible, faster, and cost-effective.