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2. Snowflake schema - Wikipedia

   en.wikipedia.org/wiki/Snowflake_schema

   In computing, a snowflake schema or snowflake model is a logical arrangement of tables in a multidimensional database such that the entity relationship diagram resembles a snowflake shape. The snowflake schema is represented by centralized fact tables which are connected to multiple dimensions .

3. Snowflake Inc. - Wikipedia

   en.wikipedia.org/wiki/Snowflake_Inc.

   In October 2014, Snowflake came out of stealth mode; at that time it was used by 80 organizations. [3] [4] Snowflake has run on Amazon Web Services since 2014, [5] [6] on Microsoft Azure since 2018, [7] and on the Google Cloud Platform since 2019. [8] [9] In June 2015, Snowflake launched its first product, its cloud data warehouse. [10]

4. Data warehouse - Wikipedia

   en.wikipedia.org/wiki/Data_warehouse

   Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...

5. An Investor's Guide to the Artificial Intelligence (AI) Value ...

   www.aol.com/investors-guide-artificial...

   Snowflake (NYSE: SNOW) has evolved from data warehousing to become a crucial AI development platform, with its latest Data Cloud innovations enabling enterprises to build and deploy AI models ...

6. Star schema - Wikipedia

   en.wikipedia.org/wiki/Star_schema

   The star schema is an important special case of the snowflake schema, and is more effective for handling simpler queries. [ 2 ] The star schema gets its name from the physical model's [ 3 ] resemblance to a star shape with a fact table at its center and the dimension tables surrounding it representing the star's points.

7. Data warehouse automation - Wikipedia

   en.wikipedia.org/wiki/Data_warehouse_automation

   Data warehouse automation (DWA) refers to the process of accelerating and automating the data warehouse development cycles, while assuring quality and consistency. DWA is believed to provide automation of the entire lifecycle of a data warehouse, from source system analysis to testing to documentation .