dataware slides2

8/14/2019 Dataware slides2

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Good

Evening to All
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III & II

IT


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Characteristics of Data-warehousing

Goals Of Data-Warehousing

Architecture Of Data-Warehousing

The Phases Involved


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Accessibility Getting required information

when ever needed

Timeliness Time taken to submit the report

Formats Formats like spreadsheets,

graphs, maps etc.,

Integrity Accuracy and Reliability of data


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Data Warehouse is a database of data

gathered from many systems and intended tosupport management reporting and

decision making.

This process of gathering data is called

Data Warehousing


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Subject oriented: Data Warehouse deals with all the subjects of

corporate data.Eg: sales, finance, customers etc

Integrated: Integrates data from different Database systems

(Heterogeneous data) to single homogeneous

data. Non-volatile: The Data Warehouse is a read only database. It

cannot be overwritten or deleted. So, itsNon-volatile.

Time variant: Historical data with chronological importance,

i.e. Historical data is maintained and analysed

for future analysis.


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To provide a reliable, single, integrated source of

information

To give end users access to their data without a reliance on

reports produced by Information System (IS)

department.

Allows to analyze corporate data, predictive models and

improve Business Intelligence.


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Four Data structures for the storage of data are: 1. DATA STORE 1, called , called Online Transaction Processing

(OLTP).

2. DATA STORE 2, called Integration Layer or Data Warehouse

3. DATA STORE 3, called Data Mart or High Processing QuerySystem (HPQS)

4. DATA STORE 4, called Online Analytical Processing (OLTP)

Three Data flow paths between the four data structures are:

1. FLOW1, from DATA STORE1 to DATA STORE 2




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The architecture is divided into threephases :

1.Extract Phase2.Transform Phase

3.Loading Phase

Transfer data

Data Store 1---------------Data Store 2There are different mechanisms for extracting that

data out of its sources. This is called Data


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The art of determining what records to extract from the

source system is frequently called Change data capture.

Some general techniques used to recognize changes to

source database tables. They are:Timestamps: The lucky among us extract data from

systems the timestamp records whenever

they are inserted or deleted.Triggers: Every time a record is inserted into,

updated in or deleted from a source table,

these triggers write a corresponding

message in a log file.FileCompares: Identify changes in your data is to

compare the file as it appears today to a

copy of how it appeared when you last

loaded the warehouse.


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Transform phase is where this data is Transformed into the required form in the

DATA STORE 2 . Some of the fundamental steps in the Transformation phase are:

1. Converting heterogeneous data to homogeneous data:--- The data in the DATA STORE 2 is from the different source

systems of DATA- STORE 1. So, the data is heterogeneous.DATA STORE 2 is called Integration Layer or Warehouse.

2. Adding Surrogate keys:--- For example, rather than using the customer number as

the key on the CUSTOMER table, you might use asurrogate key that is simply a sequential number generatedby your warehouse load programs.

3. Removing dirty data:----a. Ignoring them.

b. Rejecting bad records, but saving them in a separate filefor manual review.

c. Loadingas much of the bad record as possible and pointingout the errors for later.

4. Normalization:---A normalized database is like a flat file that is broken up into

smaller files or tables in order to store the data more


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Transformed data is sent to DATA STORE3, which is called DATAMART.

DEFINITION OF DATA MART:Data Marts are databases that share many of the

features of data warehouses but are smaller in scope.

LOADING phase constitutes several schemas. Two of them are:

Star Schema: Maintenance of data will be in one facttable and multiple dimension tables.Snow Flake Schema: Maintenance of data will be in the form of

normalized dimension tables.This DATA STORE 3 is also calledHigh Performance Query

Structures [HPQS].

DATA FLOW 3 is the transfer of data from the High PerformanceQuery Structures to the End User Reporting applications,DATA STORE4.

DATA STORE 4 is the data in the end users hands. This report inusers hands is the end of the information utility. It is, also, the


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A Centralized Data Warehouse Server is maintained at aparticular place. The transactions of all the GovernmentDepartments are transferred to the Centralized Server,Data Warehouse Server.The topology of the Network is

equated to the Architecture of the Data Warehouse asshown in the fig


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DWHS-Data Ware Housing Server

OLPS-OnLine Analytical Processing

System.

In the above example, Data from three departments areextracted and transformed to Centralized Server [DWHS].

Data Marts can answer most complex Queries andReport generation will be immediate

This Data can be checked further for any correctionsif any Incorrect data is found in the Data Ware House canbe informed to the government.

.Thus, Data Warehousing can take both

private and public sectors to a top level.


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