Snowflake 简明教程

Snowflake - Functional Architecture

Snowflake 支持结构化和半结构化数据。在完成数据加载后，Snowflake 自动组织和构建数据。在存储数据时，Snowflake 根据其智能将其划分并保存到不同微分区中。即使 Snowflake 也将数据存储到不同集群中。

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.

在功能层面上，要从 Snowflake 访问数据，需要以下组件 −

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

  1. Choose proper roles after logging

  2. Virtual Warehouse known as Warehouse in Snowflake to perform any activity

  3. Database Schema

  4. Database

  5. Tables and columns

Snowflake 提供以下高级分析功能 −

Snowflake provides the following high-level analytics functionalities −

  1. Data Transformation

  2. Supports for Business Application

  3. Business Analytics/Reporting/BI

  4. Data Science

  5. Data Sharing to other data systems

  6. Data Cloning

下图展示了 Snowflake 的功能性架构 −

The following diagram shows the functional architecture of Snowflake −

每个块中的“设置”符号可称为仓库，而 XS、XXL、XL、L、S 表示仓库的大小，需执行不同的操作。根据要求和使用情况，可以增减仓库的大小；甚至可以使其从单集群转换为多集群。

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