Data Mining Chapter2 1

7/28/2019 Data Mining Chapter2 1

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CH#3, By: Babu Ram Dawadi

3. Data Warehouse Architecture

3.1 System Process

3.2 Process Architecture


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System Process

Data warehouse are built to support large data volumes

(above 100GB of database) cost effectively

Data warehouse must be architected to support three

major driving factors:

Populating the warehouse

Day

-

to

-

day management of the warehouse

The ability to cope with requirements evolution.

The process required to populate the warehouse focus

on the extracting the data, cleaning it up and making it

available for analysis.


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Typical Process Flow

Before we create an architecture of a data

warehouse, we must first understand the major

process that constitute a data warehouse.

The processes are:

Extract and load the data

Clean and transform data into a form that can cope

with large volumes, and provide good query

performance.

Backup and achieve data

Manage queries, and direct them to the appropriate

data sources.


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Query

Process Flow Within a DW

Source

Extract & Load

Data

Warehouse

Data TransformationAnd movement

Users


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Extract & Load Process

Data extraction takes data from source systems and makes it

available to the data warehouse

Data load takes extracted data and load it into the DW.

When we extract data from the physical database, whatever form it

is held in, the original information content will have been modified

and extended over the years.

Before loading the data into the DW, the information content must

be reconstructed.

The DW extract & load process must take data and add context and

meaning in order to convert it into value

-

adding businessinformation.


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Process Flow Extract & Load

Process controlling

Is the mechanism that determine when to start extracting the data, run

the transformations and consistency checks and so on, are very

important.

The various tools, logic modules and programs are executed in the

correct sequence and at the correct time, a controlling mechanism is

required to fire each module when appropriate.

Initiate extraction

Data should be in a consistent state when it is extracted from the source

system.

The information in a data warehouse represents a

snapshot

of

corporate information, so that the user is looking at a single, consistent

version of the truth.

Guideline:

start extracting data from data sources when it represents the

same snapshot of time as all the other data sources.


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Process Flow Extract & Load

Extraction


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Process FlowExtract & Load

Loading the data Once the data is extracted from the source systems, it

is then typically loaded into a temporary data store in

order for it to be cleaned up and made consistent.

Guideline: do not execute consistency checks until allthe data sources have been loaded into thetemporary data sources.

From the temporary data store when the data iscleaned up, the data is transformed into warehouseby the warehouse manager.


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Process FlowClean & transform

This is the system process that takes the loaded data andstructures it for query performance, and for minimizingoperational costs.

The process steps for cleaning and transferring are: Clean and transform the loaded data into a structure that

speeds up queries.

Partition the data in order to speed up queries, optimizehardware performance and simplify the management ofthe DW.

Create a aggregations to speedup the common queries.


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Process FlowClean & transform

Data needs to be cleaned and checked in the followingways:

Make sure data is consistent within itself.

Make sure that data is consistent with other datawithin the same source.

Make sure data is consistent with data in the othersource system.

Make sure data is consistent with the informationalready in the DW.


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Process FlowBackup & Archive

As in operational systems, the data within the datawarehouse is backed up regularly in order to ensure thatthe DW can always be recovered from the data loss,

software failure or hardware failure.

In archiving, older data is removed from the system in aformat that allows it to be quickly restored if required.


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Process FlowQuery Management

The query management process is the system process thatmanages the queries and speeds up them up by directing queries tothe most effective data source.

The query management process may also be required to monitorthe actual query profiles.

Unlike the other system processes, query management does notgenerally operate during the load of information into the DW.

The query management facilities are: Directing Queries

The query management process determines which table delivers the answereffectively; by calculating which table would satisfy the query in the shortestspace of time.


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Process FlowQuery Management

Query management facilities. Maximizing system resources

Regardless of the processing power available to run the DW, it is also toopossible that a single large query can soak up all system processes,affecting the performance of the entire system.

The query management process must ensure that no single query can affectthe overall system performance.

Query Capture Users are exploiting the information content of the DW, which implies that

query profiles change on a regular basis over the life of a DW.

At various points in time , such as the end of week, these queries can beanalyzed to capture the new query and the resulting impact on summarytables.

Query capture is typically the part of the query management process.


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Process Architecture

The system processes describe the major

processes that constitute a data warehouse.

Now the process architecture outline a complete

data warehouse architecture that encompasses

these processes.

The complexity of each manager in a data

warehouse will vary from DW to DW.


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Three Data Warehouse Models

Enterprise warehouse

collects all of the information about subjects scanning the entire

organization

Data Mart a subset of corporate-wide data that is of value to a specific

groups of users. Its scope is confined to specific, selected

groups, such as marketing data mart

Independent vs. dependent (directly from warehouse) data mart

Virtual warehouse

A set of views over operational databases

Only some of the possible summary views may be materialized


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Components of DW Architecture

Load Manager

Warehouse Manager Query Manager

Detailed Information

Summary Information Meta Data

Data Marting


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Detailed

Information

Summary

Information

Meta data

Operational

Data

External

Data

L

O

A

D

M

A

N

A

G

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Q

U

E

R

Y

M

A

N

A

G

E

RWarehouse Manager

Data Information Decision

OLAP

Tools

Data

Differ


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Process Arch Load Manager

The load manager is the system component that performs all theoperations necessary to support the extract and load process.

This system may be constructed using a combination of off-the-selftools, C programs and shell scripts.

The size and complexity of Load Manager will vary between specificfrom DW to DW.

The effort to develop the load manager should be plannedwithin the first production phase.

The architecture of the load manager is such that it performsthe following operations: Extract the data from the source systems.

Fast load the extracted data into a temporary data store

Perform simple transformation into a structure similar to one inDW


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CH#3, By: Babu Ram DawadiLoad Manager Architecture


Controlling

Process

StoredProcedures

Copy

Management

Tool

Fast Loader

File

Structure

Temporary

Data Source

Warehouse

Structure


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Extract data from source In order to get hold of the source data it has to be transferred

from source systems, and made available to the DW.

Fast Load Data should be loaded into the warehouse in the fastest possible

time, in order to minimize the total load window.

The speed at which the data is processed into the warehouse isaffected by the kind of transformations that are taking place.

In practice, it is more effective to load the data into a relational

database prior to applying transformations and check

Simple Transformation Before or during the load there will be an opportunity to perform

simple transformations on the data.


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Process Arch Warehouse Manager

The warehouse manager is the system component that performs allthe operations necessary to support the warehouse managementprocess.

This system is typically constructed using a combination of third

party systems management software (C, shell scripts)

The architecture of the warehouse manager is such that it performsthe following operations: Analyze the data to perform consistency and referential integrity check.

Transform and merge the source data in the temporary data store in tothe DW.

Generate renormalization if appropriate.

Backup totally the data within the DW.


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Controlling

Process

Stored

Procedures

Backup/Reco

very Tools

SQL Scripts

Temporary

Data Source

Warehouse

Structure

Schema

Warehouse Manager

Warehouse Manager Architecture

Guideline: do not

load data directly

into the DW tablesuntil it has been

cleaned up. Use

temporary tables

that emulate the

structures with in

the DW.


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Create Index & Views The warehouse manager has to create indexes against the information

in the fact or dimensional table.

The overhead of inserting a row into a table and indexes can be higher

with a large number of rows than the overhead of recreating the indexesonce the rows have been inserted.

Therefore it is more effective to drop all indexes against tables prior toinserting large rows

The fact tables are large tables, so the warehouse manager createsviews that combine a number of partitions into a single fact table.

It is suggested that, we create a few views, corresponding to meaningfulperiods of time within the business.


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Generate the summaries: Summary information is necessary in any organization because

the higher level officers dont want to see the detailedinformation.

The summary information will be helpful to them for decisionmaking.

Summaries are generated automatically by the warehousemanager: i.e. it is executed every time data is loaded.

The actual generation of summaries is achieved through the useof embedded SQL in either stored procedure (Trigger) or Cprograms.

a Command sequence such as:

Create table {} as select {.} from {.} where {..}


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Process Arch Query Manager

The query manager is the system componentthat performs all the operations necessary tosupport the query management process.

The architecture of a query manager is such thatit performs the following operations: Direct queries to the appropriate tables

Schedule the execution of user queries.

The query manager also stores query profiles toallow the warehouse manager to determinewhich indexes are appropriate.


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Process Arch Query Manager

Query

Redirection Via C

tools, RDBMS

Stored Procedure

(Generate Views)

Query

Management

Tools

QueryScheduling

via C tool or

RDBMS

Meta Data Detailed

Information

Summary

Information


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Process Arch Detailed Information

This is the area of the data warehouse that stores all the detailedinformation in the starflake schema.

All the detailed information is held online the whole time, but aggregated tothe next level of detail. And then the detailed information is offloaded into

the tape archive.

If the business information for detailed information is weak or very specific,it may be possible to satisfy it by storing a rolling three-month detailedhistory.

Guideline: determine what business activities require detailed transaction

information, in order to determine the level at which to retain detailinformation in the DW.

If the detailed information is being stored offline to minimize the disk storagerequirements, make sure that the data has been extracted, cleaned up, andtransformed into a starflake schema prior to archiving it.


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Detailed

Information

Summary

Information

Meta data

Operational

Data

External

Data

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RWarehouse Manager

Data Information Decision

Detailed info. In

archived dataOLAP Tools

Data Differ


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Detailed information can be managed by

the topics:

Data warehouse schemas Fact data

Dimension data

Partitioning data


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Star

customer custId name address city

53 joe 10 main sfo

81 fred 12 main sfo

111 sally 80 willow la

product prodId name price

p1 bolt 10

p2 nut 5

store storeId city

c1 nyc

c2 sfo

c3 la

sale oderId date custId prodId storeId qty amt

o100 1/7/97 53 p1 c1 1 12

o102 2/7/97 53 p2 c1 2 11

105 3/8/97 111 p1 c3 5 50


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Star Schema

sale

orderId

date

custId

prodIdstoreId

qty

amt

customer

custId

nameaddress

city

product

prodId

nameprice

store

storeId

city


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Cube

sale prodId storeId amt

p1 c1 12

p2 c1 11

p1 c3 50

p2 c2 8

c1 c2 c3

p1 12 50

p2 11 8

Fact table view:Multi-dimensional cube:

dimensions = 2


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A Sample Data Cube

Total annual sales

of TV in U.S.A.Date

Countr

ysum

sum

200 150 63 37

TV

VCRPC

1Qtr 2Qtr 3Qtr 4Qtr

U.S.A

Canada

Mexico

sum

450


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Process Arch Summary Information

Summary information is essentially a replication of information already inthe data warehouse.

The implication of summary data is that the data: Exists to speed up the performance of common queries

Increases operational cost May have to be updated every time new data is loaded into the DW

May not have to be backed up, because it can be generated fresh from thedetailed info.

The size of data that needs to be scanned is an order of magnitude smaller,this results in an order of magnitude improvement to the performance of thequery.

On the negative side there is an increase in operational cost, for creatingand updating the summary table on a daily basis.

Guideline1: avoid creating a summary that require more than 200centralized summary tables on an ongoing basis.


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Process Arch Summary Information

Summary infocontd Guideline2: inform users that summary table accessed

infrequently will be dropped on an ongoing basis.

Metadata

Data Marting A data mart is a subset of the information content of a DW that is

stored in its own database, summarized or in detail.

Data marting can improve query performance, simply bereducing the volume of data needs to be scanned to satisfy a

query. Data marts are created along functional or departmental lines, in

order to exploit a natural break of the data.


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CH#3 By: Babu Ram Dawadi

Multi-Tiered Architecture

Data

Warehouse

Extract

Transform

Load

Refresh

OLAP E i

Analysis

QueryReports

Data mining

Monitor

&

IntegratorMetadata

D t S F t E d T l

Serve

Data Marts

Operational

DBs

other

sources

D t St

OLAP Server

Documents

Data Mining Chapter2 1