NW
TX1 APPL
DA-MW
MSG-MW
TX2 APPL
DA-MW
MSG-MW
TX3 APPL
DA-MW
MSG-MW
TX4 APPL
DA-MW
Extract from Database
OLTP1
OLTP2
OLTP3
OLTP4
Transactional Repositories
ASP / JSP
Service Brokers
> Works against information to support the Business View. The applications work within the confines of the Information architecture, creating and consuming the data elements, rules and definitions of that architecture view.
10 TB 5 TB
# of Concurrent Queries
15+ way Joins + OLAP operations + Aggregation + Complex “Where”
constraints + Views Parallelism
Simple Star
3-5 Way Joins
Gartner Magic Quadrant for Data Warehouse DBMS Servers, 2006 Feinberg, Hardcastle, Butler, Dawson (8/25/2006)
The Magic Quadrant is copyrighted 9/12/06 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner's analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the "Leaders" quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
> The data is worked on by the applications, used by the business.
• 应用
> What functions and interrelations of functions do the applications have and need? Sales, Marketing, Pricing, Manufacturing, Customer Management.
Financial
Travel
Retail
Insurance
AoyamaShoji
Manufacturing
Teradata 数据仓库技术的领导者
软件
硬件
Gartner Magic Quadrant for Data Warehouse DBMS, 2006 Feinberg & Beyer (9/2006)
• 技术
> The bit IT cares about most. The easiest to get WRONG because we don’t concentrate on the other aspects of architecture FIRST!
> What do we have and need to support the other 3 Views without limitation?
Enterprise Service Bus
Duplicate
MSTG-rMaW nsacMStGi-oMWn
Event Notification
Business Rules
DA-MW
DA-MW
MSG-MW
Event Detection
DA-MW
Business Process Automation
Processing
QD EDW — B RS
Analytic & Decision Making Repositories
Reference Architecture – Data loading View
Frontline Users
Corporate Memory
ORDER
ORDER NUMBER ORDER DATE STATUS
ORDER ITEM BACKORDERED QUANTITY
ORDER ITEM SHIPPED QUANTITY SHIP DATE
ITEM ITEM NUMBER QUANTITY DESCRIPTION
当前 转换 目标
技术 应用 信息 业务
逻辑层 方案
项目
EDW 应用逻辑架构
操作型源数据影像
多功能模型 历史数据 经转换后
视图 逻辑数据集市 依赖型数据集市 分析型知识库
Tier 1 Operational Image
Of
Tier 2
Single Version
CUSTOMER
CUSTOMER NUMBER CUSTOMER NAME CUSTOMER CITY CUSTOMER POST CUSTOMER ST CUSTOMER ADDR CUSTOMER PHONE CUSTOMER FAX
50% of Top 10 Global Retailers
40% of Top 10 Global Commercial
& Savings Banks
FORTUNE Global Rankings, July 2006
• Leading industries
> Banking/Financial Services > Government > Insurance & Healthcare > Manufacturing > Retail > Telecommunications > Transportation Logistics > Travel
成功由好的架构设计方法开始
• 业务
> What is the business model, where is it going, how does it plan to get there?
> The requirements. The business process.
• 信息
> What data do we have and need to support the Business View? Information is also calculations and rules. Typically we see Logical & Physical data models here, all subject areas of the business.
• 全球员工超过5,500名
Teradata 市场份额
Teradata Top 10
90% of Top 10 Global Telco Firms
70% of Top 10 Global Airlines
60% of the Top 10 Transportation Logistic Firms
Query Complexity
Query Data TB’s Volumes
Workload Mix
Agenda
• Teradata简介 • 架构设计原理 • 整体架构说明 • ETL架构说明
架构立方
逻辑架构层
操作的顺序
技术 应用 信息 业务
逻辑层 方案 项目
定义的 等级
当前 转换
目标
起
经
止
物理
Teradata 系统扩展能力
Data Storage (raw, user data)
20 TB
Multiple, Integrated Stars and Normalized
15 TB
1,000
Data Model Sophistication
Normalized
Multiple, Integrated Stars
Streaming Batch
Data Acquisition & Integration