Data Warehouse Series--(1) Data Warehouse popularization

Data Warehouse Introduction

a good Data warehouse design is The cornerstone of BI Analytics.

Data Warehouse [DW] .  

decision procedure , Span style= "font-size:14px;font-family: ' Microsoft Jas Black ', ' Microsoft Yahei '; Color:rgb (255,0,0);" > theme-oriented , integration , gradient , persistent data collection .

Unlike traditional databases, DW is a multidimensional database. The two basic elements of a data warehouse store are dimension tables and fact tables.

fact table: a table that reflects the core of the business, the table stores the key data related to the business, which we call "measures", is the main field for future calculations and statistics. dimension table:a table that stores non-core information related to the business. The fact table is associated with the dimension table through the primary foreign key, and there are two types of structures: Star structure and snowflake type structure. star-shaped structure:A fact table joins the structure of one or more dimension tables, and the dimension table no longer associates other dimension tables. Snowflake Structure: refers to a fact table associated with one or more dimension tables, and the dimension table is also associated with other dimension tables, which form a multi-layered structure, called snowflake type. slices: a technique used to limit the analysis space in a dimension to a subset of data in a data warehouse.  Cut into cubes:a technique used to limit the analysis space in multiple dimensions to a subset of data in a data warehouse.

Data Warehouse Architecture

Data Source: Is the basis of data Warehouse system, is the data source of the whole system.

Data storage and management: The real key to data warehousing is the storage and management of data. For the existing business systems of data, extraction, cleanup, and effectively integrated, organized according to the theme. Load into the Data warehouse. Data warehouses can be divided into enterprise-level data warehouses and departmental data warehouses (often referred to as data marts) in terms of data coverage.

OLAP server: Effective integration of the data required for analysis, organized by multidimensional model, for multi-angle, multi-level analysis, to discover trends.

Front-end tools: mainly include a variety of reporting tools, query tools, data analysis tools, data mining tools and a variety of data warehouse or data mart based application development tools.

Five-step process for enterprise data warehousing

1. Determine the subject

2. Determine the measurement

3. Determine the granularity of facts

4. Determining Dimensions

5. Create a fact table

This article is from "sole weeping" blog, declined reprint!

Data Warehouse Series--(1) Data Warehouse popularization