Download Warehouse Architecture and Design Principles

SAS® DATA MANAGEMENT – COURSE OVERVIEW
Warehouse Architecture and
Design Principles
 Duration
3 days
 Delivery
Classroom
 Course code
WADPRIN
 Online registration
www.sas.com/uk/education/
courses/wadprin.html
This 3-day course provides a broad
coverage of the architecture and detailed
physical design of a data warehouse
- the storage, management and exploitation of data in an integrated information
architecture. Anyone attending should
already have an understanding of systems analysis and practical knowledge
of the relevant SAS system features (see
pre-requisites).
• data warehouse or data mart project
experience as an implementer (WCO
or SAD) or analyst (WAN)
Objectives
• SAS/Warehouse Administrator
software
®
After attending this course attendees
will be able to create architecture and
designs for an integrated information
architecture,including:
• the Enterprise storage layer of a
data warehouse, based on the most
common physical models used
• OLAP data marts
• data mining data marts
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• processes (ETL - Extract, Transform
and Load) used in loading the
Enterprise layer and in maintaining
data marts
• ability to read entity-relationship
diagrams
• knowledge of the basic principles of
designing applications and data.
Useful but not essential is
experience with:
• databases and/or SPDServer
• client/server SAS features
• data mining
• web technology
• data warehouse applications
• projects as warehouse architect
(WAC)
• entity-relationship modeling
techniques and tools. SAS System
Modules Used.
Course Topics
• management and publishing of
metadata
The course will consist of three parts:
 0845 402 9902
• managing the environment, including
archival and backup of data
2.Demonstrations of practical data
warehouse techniques
 [email protected]
• basic principles of planning the
infrastructure (hardware and software)
3.Exercises based on a case study.
• considerations for optimal
performance in large data
warehouses.
Introduction:
 www.sas.com/uk/education
1.Presentations
The presentations cover the following:
Prerequisite Skills
• Concepts of an integrated information
architecture
Essential:
• Role of the ‘Data Warehouse
Architect’
• familiarity with a wide range of SAS
software used in data warehousing,
SAS data management and data
access features, SAS OLAP viewing/
reporting tools
• Evolution of a data warehouse
environment.
See over for Training Path
Subject Modeling :
• Data models used in planning a date
warehouse
• Subject models and how to define
them
• Transformation – validation &
cleansing, integration, enrichment,
transformation, transfer
• Optimisation strategy
• Load – load techniques including
‘slowly changing dimensions’
• Case study: optimisation
considerations.
• Common optimisations used in the
data warehouse
• Case study exercise: defining a
Subject Model.
• Case Study: Extract processing,
Load processing
Designing the Enterprise Layer:
• Use of ‘standard techniques’ in ETL.
• Definition of Logical modeling versus
Physical modeling
Security Considerations
• Considerations for the end-to-end
process
• Preparing a Logical model
• Defining Security requirements
• Scheduling
• Preparing a Physical model, choosing
the model type
• Architecture considerations, main
types of Security
• Distribution of data
• Defining and using Normalised data
models
• Design considerations for Security.
• Backup and Restore.
Metadata
Infrastructure
• What is metadata
• Considerations for hardware choices,
software choices
• ETL administration
• Defining and using Dimensional data
models
• Defining and using metadata.
• Specific features of Dimensional
models: facts and measures,
conforming dimensions, time and
history, querying a star schema
• Role of the data marts in an
integrated information architecture
• Structure of data marts
Process Architecture and Design:
• Data mart physical models
• Planning the Process Architecture
• Aggregates in data marts
• Considerations for Process Design:
source identification, business rules,
data quality, security
• Data mart design.
Optimizing the Enterprise Layer of
the Data Warehouse
• ETL – acquiring, preparing and
loading data
• Scalability impacts, performance
trade-offs
• Extraction – business rules, changed
data capture
• Aging, Archiving and Recovery
• Estimating size, capacity planning.
Data Marts
• Case study: Dimensional modeling.
Process Architecture and
Design part 2: Data Warehouse
Management
Summary and Close
• The exercises are based on a case
study and include group activities
• Attendees work on requirements
derived from real projects to create
physical data models and design
warehouse processes.
Examples demonstrated during the
class include a complete working
ETL process for an enterprise-style
data warehouse with slowly changing
dimension history.
Training Path for Data Integration Architects
Warehouse Architecture and
Design Principles
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