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 divides it on his intelligence 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 functionalities −

  • Data Transformation

  • Supports for Business Application

  • 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.

Functional Architecture
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