Snowflake - Functional Architecture

Snowflake supports structured and semi-structured data. Snowflake organizes and structures the data automatically once data loading is completed. While storing the data, Snowflake spanides it on his intelpgence and saves into different micro-partitions. Even Snowflake stores data into different clusters.

At functional level, to access data from Snowflake, the following components are required −

Choose proper roles after logging

Virtual Warehouse known as Warehouse in Snowflake to perform any activity

Database Schema

Database

Tables and columns

Snowflake provides the following high-level analytics functionapties −

Data Transformation

Supports for Business Apppcation

Business Analytics/Reporting/BI

Data Science

Data Sharing to other data systems

Data Cloning

The following diagram shows the functional architecture of Snowflake −

The symbol of "settings" as in each block can be referred as Warehouse and XS, XXL, XL, L, S as sizes of warehouse requires to perform different operations. Based on requirement and usage, the size of a warehouse can be increased or decreased; even it can be converted from single cluster to multi-clusters.