Concepts of Data Warehousing
Concepts of data warehousing have changed significantly over the past decade. Tradtionally, data warehouses were on-premise solutions that for decades managed to meet the data requirements of the day with some serious maintenance and TLC. However, the data explosion of the last decade has made the on-prem model obsolete -- they can simply not scale in a timely or cost-effective manner. The new paradigm is the cloud-built data warehouse, which naturally can take advantage of the cloud's MPP (massively parallel processing) capabilities to increase scalability and performance while reducing overhead and maintenance (on some cases to zero), and rapidly speeding time to value.
Concepts of Data Warehousing and Snowflake
Snowflake is the industry's first data warehouse built from the ground up in the cloud. Snowflake’s unique data warehouse architecture provides full relational database support for both structured and semi-structured data in a single, logically integrated solution. Snowflake is a DWaaS (data warehouse-as-a-service), which delivers separate compute, storage, and cloud services that can independently change and scale -- and metadata processing does not compete with the query compute resources
Snowflake supports enterprise-wide data warehouse requirements for companies of any size, from SMBs to global Fortune 1000 enterprises, offering practically unlimited concurrency. It can also serve as a powerful back-end platform for developers creating modern data-driven applications.