1. Supervisor : Prof . Abbdolahzadeh
Amirkabir University
Data Warehouse Design Architectures
Morteza Zaker
Supervisor : Prof . Abbdolahzadeh

2. Presentation plan Introduction Modeling issues Conclusions
Data Warehouse Architecture
Concepts of dimensional model
History of Data Warehouse
Modeling issues
Conclusions

3. DW and OLAP – general concepts
Data Warehouses – contain historical data for supporting decision-making process
On-Line Analytical Processing systems - facilitate manipulation of DW data
DW and OLAP require clear definition of facts, dimensions, and hierarchies
DW logical level design based on star/snowflake schema

4. Data Warehouse Architecture
Data Flow Architectures
Single DDS
NDS+DDS
ODS+DDS
System Architecture
Federated Architectures
ETL Architectures

5. Extract and transform and Load (ETL) then bring data from various source system into a stage area.
ETL Integrate and transform stage’s data then load it to dimensional data store (DDS).
when loading data into DDS ,(DQETL) do various rules to check data then bad data push into DQ data base for reporting and correcting .Bad data Automatically be corrected or tolerated if it can be needed.
User Can get data via several front-end tools and applications.
Some Application operate on Multidimensional format. So data in the DDS is Loaded to Multidimensional database( MDB ).
Multidimensional is a form of database that data is stored like a cub. Cells of cube represent number of variable which is called Dimensions. Value of dimension show when and where business event happened.
ETL system is managed by Control system based on the rules in the metadata.
Metadata is a database that contain information about the data structure data usage-quality rules and other information about data.
Audit system is used for understanding what happen during ETL process and then logs system oprenation into Metadata database.
The Data is examined to realize characteristic of data by data Profiler.
Data profiler analyze data to find out that for example how many row does Table has? And which one is Null and so on.
Spreadsheet
Source system are OLTP system that contain data which is loaded to DW .
OLTP : Capture and store Business Transaction online.
Pivot tables
Ad Hoc query
Metadata
Data Profiler
Control
system+Audit
reports
Source
System
ETL
stage
DDS DQ
ETL
MDB
analytic
reports DQ
correction
Data Mining
Other BI
Application
reports

7. Single DDS
Extract data from several source system
Push it in stage area Stage area could be a database or files system.
Stage is necessary because of lacking memory space and so on .
Application
Metadata
Control
system+Audit s1
DDS
Source
System
ETL+DQ
Advantage of Single DDS is simple to design , because the data from the stage is loaded straight into the dimensional data store, without going to any kind of normalized store. It is good for system which has just one source or just has one dimension.
The main ِDisadvantage is that it is more difficult, in this architecture, to create a second DDS. The DDS in the single DDS architecture is the master data store. s2
Stage
MDB
Control system + Audit manage ETL system concurrently .
Log ETL process to Metadata file or database
Metadata contain Data Structure and data processing within data warehouse
Second ETL package pick up data from Stage and Integrates them.
Apply some Data Quality rules
Puts consolidated data into a DDS
Application

8. NDS + DDS Data storage = Stage, NDS & DDS
Control
system+Audit
Metadata
Application
Data storage = Stage, NDS & DDS
Core DW Store = Normalized & Dimensional Format
Data Marts = 1 to N Data Marts in each DDS
ETL Engine = 4 ETL Package
NDS Contain Master table and transaction Table
Master Table  Dimensions in DDS
Transaction table  Facts in DDS s1
DDS s2
NDS-ETL
+DQ
NDS
DDS-ETL
DDS s2
Stage
Application
MDB
NDS is the in front of DDS and NDS is our master data .Master data contain all historical nad structral data .
DDS is our Transactional data and just could contain Single years of data .
Application

9. ODS is hybrid data store so User can access data from ODS
ODS + DDS
We have got
Data storage = Stage, ODS & DDS
Core DW Store = Normalized & Dimensional Format
Data Marts = 1 to N Data Marts in each DDS
ETL Engine = 4 ETL Package
ODS Contain Master table and transaction Table but it is not Master data store
Master Table  Dimensions in DDS
Transaction table  Facts in DDS
The advantage of this architecture is that The third normal form is slimmer than the NDS because it contains only current values.
In this architecture we have a central place to integrate, maintain, and publish master data.
The normalized relational store is updatable by the user application.
The main ِDisadvantage is that it is more difficult, in this architecture, to create a second DDS. The DDS in the single DDS architecture is the master data store.
Control
system+Audit
Metadata
Application s1
DDS s2
ODS-ETL
+DQ
ODS
DDS-ETL
DDS s2
Stage
Application
MDB
Application
ODS is hybrid data store so User can access data from ODS

10. Federated DW FDW FDW FDW Application Application Application ETL EII
DM3
DW2
DW1
DW2
DW3
DM2
DW1
DM1
Relational DW
EII(Extract Information Integration)
is a method to integrate data by accessing different source systems online and aggregating the outputs on the fly before bringing the end result to the user.
All 3 DWs must be standardized as the same structure.
Third normalized DW
Data marts in the same Data warehouse is nonintegrated data marts.
They can be dimensional, normalized, or neither
Dimensional DW
The FDW ETL needs to match the Updating time frequency of the source DWs.
The FDW ETL needs to integrate the data from source DWs based on business rules.
Duplicate records need to be merged.
Subject area in here is very narrow that the source DWs.

11. System Architecture

12. ETL Architectures

13. There are two different types of database software
Main Issues that must be considered
There are two different types of database software
Symmetric multi processing(SMP)
It is a databas system that runs on one or more machines with several identical processors sharing the same disk storage. The database is physically located in a single disk storage system. Examples of SMP database systems are SQL Server, Oracle, DB/2, Informix, and Sybase
2. Massively parallel processing (MPP)
It is a database system that 20uns on more than one machine where each machine has its own disk storage. The database is physically located in several disk storage systems that are interconnected to each other. An Examples of MPP database systems are Teradata, Neoview, Netezza.
MPP database system is faster and more scalable than an SMP database system. In an MPP database system, a table is physically located in several nodes, each with its own storage.

14. Research challenges (1)
Spatial measure aggregations considering
Their types
Distributive – reuse of aggregates, e.g., spatial union
Algebraic – additional treatments for reusing aggregates, e.g., center of n points
Holistic - new calculation with a row data, e.g., equi-partition
Topological relationships between hierarchy levels
Types of hierarchies