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 −
-
Choose proper roles after logging
-
Virtual Warehouse known as Warehouse in Snowflake to perform any activity
-
Database Schema
-
Database
-
Tables and columns
Snowflake 提供以下高级分析功能 −
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
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.
