Consolidating data using datamarts
So far, most cloud use cases focus on application servers.
Yet, there are many reasons user organizations also need a “database cloud” for database management systems (DBMSs), data, and data-driven applications such as business intelligence (BI) and data warehousing (DW).
The goal of BI is to use technology to transform data into actionable insights and help end users make more informed business decisions, whether tactical or strategic in nature.
This article clearly defines both of these important terms before elaborating on their respective use cases and architectural features.
The following are some important distinguishing features of a Data Mart: Data Warehouse Defined A Data Warehouse is an enterprise-wide repository of integrated data from disparate business sources, systems, and departments.
Ralph Kimball argues that the best approach is to begin with the most important business aspects or departments, from which Data Marts oriented to specific lines of business emerge.
Over time, enterprises can merge their Data Marts to form a Data Warehouse as required.
Only a data warehouse with a cloud-built data architecture makes it possible to support your current and future data analytics workloads at any scale.
Snowflake’s patented multi-cluster, shared data architecture makes it possible to support any scale of data, workload, and users.