Data Warehousing Final

7/31/2019 Data Warehousing Final

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TEAM

Rachana Kola 31

Tasneem Taj 30

Vijay Kumar 29

Shilpa Kasani 28

Ankita Golchha 27

Bharat Jain 26


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Data warehousing

Data Warehousing is a database used for reporting &analysis.

It focuses on data storage.

Essential components of Data warehouse system.

Data warehouse can be subdivided into data marts.


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Characteristics of data warehouse

Conceptualview.

Unlimiteddimensions.

Dynamicsparse matrix

handling.

Client /server

architecture.

Accessibility&

transparency.


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OLTP & OLAP

OLTP : Online transaction processing It is characterizedby a large no. of short online transactions. ( Insert ,Update , Delete )

OLAP : Online Analytical Processing It is characterizedby relatively low volume of transactions.


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OLTP v/s OLAP

OLTP (Operational

system)

OLAP (Data warehouse

system)

Sources of data Operational data. Consolidation data.

Purposes of data Control and runfundamental businesstasks.

Planning , problemsolving & decisionmaking.

What the data reveals Snapshot . Multi dimensional view.

Inserts & updates Short and fast. Periodic long running.

Queries Simple queries. Complex queries

Processing speed Very fast. Depends on the amount

of data involved.Space requirements Relatively small. Large due to aggregation

and history data.

Database design Highly normalized. De normalized.

Backup & recovery Backup essential

regularly.

Reloading OLTP data as a

recovery method.


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ARCHITECTURE

External datasources

EXTRACT

CLEANTRANSFORM

LOAD

REFRESH

MetadataRepository

Data warehouse

Reports

OLAP

Data Mining

Operationalsystems

Serves


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COMPONENTS

3 main systems required : Source systems

Data staging area

Presentation servers

Operational data :

Internal data

External data

Load manager :

Simple transformation of data to prepare the data for entryinto the warehouse.


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CONTD..

Warehouse manager : Analysis of data. Transformation & merging of data. Backing up & archiving of data.

Detailed summarized archived data.

Meta data : Meta data means data about data. Extraction & loading process. Warehouse management process. Query management process.

End user access tools.


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ETL PROCESS

Extract

Transform

Cleansing

Loading


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IMPORTANT TERMS

Drill down

Roll up

Aggregation

Granularity


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DIMENSIONAL DATA MODEL Dimensional data model is most often used in data warehousing

systems.

The objective of dimensional modeling is to represent a set of business

measurements in a standard framework that is easily understandable by

end users.

The main components of a Dimensional Model are Fact Tables and

Dimension Tables.

A fact table is a table that contains the measures of interest. A dimension is a structure usually composed of one or more hierarchies

that categorizes data.


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Example of dimensional model


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STAR SCHEMA The star schema is also called star-join

schema, data cube, or multi-dimensional

schema.

It is the simplest style of data

warehouse schema.

The star schema consists of one or more

fact tables referencing any number

of dimension tables.

A star schema classifies the attributes of

an event into facts (measured

numeric/time data), and descriptive

dimension attributes (product id,

customer name, sale date) that give the

facts a context.


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SAMPLE STAR SCHEMA


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Advantages of star schema

The main advantages of star schemas are that they:

Provide a direct and intuitive mapping between the business entities

being analyzed by end users and the schema design.

Provide highly optimized performance for typical star queries.

Are widely supported by a large number of business intelligence

tools, which may anticipate or even require that the data-warehouse

schema contain dimension tables


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SNOW FLAKE SCHEMA

That dimensional table is normalized into multiple lookuptables each representing a level in the dimensional hierarchy.

Each point of the star explodes into more points.

Extension of the star schema.


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SAMPLE SNOWFLAKE SCHEMA


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FACT CONSTELLATION SCHEMA


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FACT CONSTELLATION SCHEMA Shaped like a constellation of stars

More complex than star or snowflake

With each star schema it is possible to construct factconstellation schema

By splitting the original star schema into more starschemes each of them describes facts on another level ofdimension hierarchies


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PERSONAL PRODUCTIVITY

APPLICATIONS Useful for manipulating and presenting data on

individual PCs.

Developed for a standalone environment Address applications requiring only small volumes of

warehouse data.


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DATA QUERY & REPORTING

Data access through simple, list-oriented queries, and thegeneration of basic reports.

Provide a view of historical data .

Do not address the enterprise need for in-depth analysisand planning.


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PLANNING & ANALYSIS

Address such essential business requirements.

Referred to as on-line analytical processing (OLAP)applications.

Mandates that the organization look not only at pastperformance but, more importantly, at the futureperformance of the business.

The combined analysis of historical data with futureprojections is critical to the success of today'scorporation.


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Advantages of data warehousingProvides business

users a customer centric view of the

companysheterogeneous data.

Added value tocompanys customersthrough better access

to information.

Historicalinformation.

Enhanced dataquality.

Supplements disasterrecovery plans.

One stop shop.

Provides saving inbilling processes,

reduces fraud lossesetc.


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Disadvantages of data warehousing

Not optimal for unstructured data.

Data warehouses get outdated relatively quickly.

Duplicate functionality between data warehouses &operational systems.

Extremely expensive. Costs :

Time spent in careful analysis.

Design & implementation.

Hardware costs. Software costs.

On going support & maintenance.


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Conclusion

Data warehousing is necessary to analyze the businessneeds, integrate data from several sources, model thedata in an appropriate manner to present the businessinformation in the form of dashboards and reports.


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Data Warehousing Final

Documents

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