Snowflake analytic data warehouse architecture is a hybrid of traditional shared-disk database architectures and shared-nothing database architectures. Similar to shared-disk architectures, Snowflake uses a central data repository for persisted data that is accessible from all compute nodes in the data warehouse. But similar to shared-nothing architectures, Snowflake processes queries using MPP (massively parallel processing) compute clusters where each node in the cluster stores a portion of the entire data set locally.This approach offers the data management simplicity of a shared-disk architecture, but with the performance and scale-out benefits of a shared-nothing architecture.Snowflake’s unique architecture consists of three key layers:● Database Storage● Query Processing● Cloud ServicesDatabase StorageWhen data is loaded into Snowflake, Snowflake reorganizes that data into its internal optimized, compressed, columnar format. Snowflake stores this optimized data in cloud storage.Snowflake manages all aspects of how this data is stored — the organization, file size, structure, compression, metadata, statistics, and other aspects of data storage are handled by Snowflake. The data objects stored by Snowflake are not directly visible or accessible by customers; they are only accessible through SQL query operations run using Snowflake.Query ProcessingQuery execution is performed in the processing layer. Snowflake processes queries using “virtual warehouses”. Each virtual warehouse […]
Read More +Overview of the architecture of Snowflake Analytic Data Warehouse
