Snowflake has a unique storage architecture that is designed to provide high-performance analytics and efficient data management. The storage hierarchy in Snowflake consists of three layers: the database, the micro-partition, and the storage layer.
At the top of the hierarchy is the database layer, which contains all of the tables, views, and other database objects. Each database in Snowflake is made up of one or more schemas, which in turn contain the actual tables and other objects.
The next layer down is the micro-partition layer. In Snowflake, data is stored in micro-partitions, which are small, self-contained units of data that can be scanned independently. Each micro-partition contains a range of rows from one or more tables, and each row is compressed and encoded for efficient storage.
Finally, at the bottom of the hierarchy is the storage layer. This is the layer of physical storage, which consists of cloud-based object storage such as Amazon S3. Snowflake uses a unique storage architecture that separates compute from storage, allowing for elastic and efficient scaling of both resources.
The storage hierarchy in Snowflake is designed to provide high-performance analytics and efficient data management. By separating compute from storage and storing data in micro-partitions, Snowflake is able to provide fast, efficient querying and analysis of large datasets, while also allowing for easy management and scaling of the underlying storage infrastructure.
