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OLTP OLAP

On Line Transaction Processing

System

On Line Analytical Processing

System

Instant data can be viewed to

User

Ex: ATM (Oracle)

Instant data can be viewed to

User

Ex: Data warehouse

Data Analytics (Teradata)

Big Data: Data beyond our capacity and processor.

Storage, Process data

Data Generator Factors: Sensors, CC Camera,

S/N(FB), Hospitality Data.

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Business Intelligence Report

System Data Size Process Time

DSS Huge Data Minutes / Hours

Adhoc Queries Less Data Minutes / Hours

Tactical Queries Very Less Data Few Minutes

BAM(Business Activity Model) Less Data Minutes

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Data Analytics

• OLAP

• Data Mart

• Data Mining

• Active Intelligence

Teradata became more common incorporations where enterprise-wide detail datamay be used in on-line analytical processing tomake strategic and tactical business decisions.

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• Data warehouses have become more common in corporations where enterprise-wide

detail data may be used in on-line analytical processing to make strategic and tactical

business decisions.

• Data Warehouses often carry many years worth of detail data so that historical trendsmay be analyzed using the full power of the data.

• Many data warehouses get their data directly from operational systems so that the data is

timely and accurate. While data warehouses may begin somewhat small in scope andpurpose, they often grow quite large as their utility becomes more fully exploited by theenterprise.

• Data Warehousing is a process, not a product. It is a technique to properly assemble andmanage data from various sources to answer business questions not previously possible orknown.

DDDData ata ata ata MMMMartartartart

A data mart is a special purpose subset ofenterprise data used by a particulardepartment, function or application.Collection of some specific data from ahuge data.

For Ex: Suppose I have a table whichcontains whole India’s Rice productiondetails

If my intention is Only Maharastra data,then I can have a small table which can onlycontain Maharastra data.

Data Mart is of 3 types

1. Physicala) Dependentb) Independent

2. Logical(View)

Production Table of whole

India Production Table

of only MH

Data

File

Independent

Dependent

View for

Maharastra Data

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Data

Warehouse

Integrated

Time VariantNon Volatile

Subject

Oriented

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Data is categorized and stored by business subject

rather than by application

Equity

Plans Shares Customer

financial

information

Savings

Insurance

Loans

OLTP Applications Data Warehouse Subject

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OLTP Applications

Savings

Current

accounts

Loans

Data on a given subject is defined and stored once.

Customer

Data Warehouse

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Data is stored as a series of snapshots, each

representing a period of time

Time Data

Jan-97 January

Feb-97 February

Mar-97 March

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Typically data in the data warehouse is not updated or deleted.

Insert Read Read

OperationalWarehouse

Load

Update Delete

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Data Warehouse:•Holds multiple subject areas•Holds very detailed information•Works to integrate all data sources•Does not necessarily use a dimensionalmodel but feeds dimensional models.

Data Mart•Often holds only one subject area- forexample, Finance, or Sales•May hold more summarized data (althoughmany hold full detail)•Concentrates on integrating information froma given subject area or set of source systems•Is built focused on a dimensional model usinga star schema.

DDDData ata ata ata MMMMiningininginingining

• Data Mining is an analytic process designed toexplore data (usually large amounts of data -typically business or market related - also known as"big data") in search of consistent patterns and/orsystematic relationships between variables, andthen to validate the findings by applying thedetected patterns to new subsets of data.

• The ultimate goal of data mining is prediction.

• The process of data mining consists of three stages:

(1) the initial exploration: This stage usually starts withdata preparation which may involve cleaning data,data transformations, selecting subsets of records

(2) model building or pattern identification withvalidation/verification,

(3) deployment (i.e., the application of the model tonew data in order to generate predictions).

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• Make better, faster decisions using near-real time data and insights

• Teradata's customers lead the way on innovative uses of active data to drive business value.

• The most popular uses of Active Intelligence are to provide Active Dashboards, Active Customer Management/Sales/Service and Active Operations.

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Passive Intelligence Active Intelligence

Used by Business analysts, managers and executives Used by Business analysts, managers, executives and

front-line operation staffs, partners and suppliers.

Historical Data. Near Real time data and Historical data.

Human Analysis and reporting. Human guided and automated analysis and reporting

Human Action taking. Human and automated action taking.

Role based classic dashboard (Historical trends, reports,

virtualization, drill down).

Role bases active dashboard (Alerts, Early warning, real-

time and historical trend virtualization, drill down and

guided intelligence, predictions and recommendations).

BI tool for human analysis and reporting. • BI services supporting on-demand requests for

intelligence from operational applications, processes,

portals and mobile devices.

• Recommendation services supporting on-demand

requests for automated analysis from within

operational applications and processes.

Human escalation of alerts if not acted upon. Automated escalation of alerts if not acted upon.

OLAP

AAAActive ctive ctive ctive DDDData ata ata ata wwwwarehousearehousearehousearehouseDepending upon the freshness of data, data warehouse isof two types.

• Active data warehouse

• We need fresh data to do active event for takingdecision at run time.

• ADW is the timely, integrated, logically consistentstore of detailed data available for tactical andstrategic driven business decisions.

• Example: Provide discount coupons at Casinoaccording to the Roller’s success or failure.

• Passive Data warehouse

Operational database

(OLTP)

Accurate and up to minute data

OLAPOperational database

(OLTP)

Hourly/Daily/Weekly/Monthly

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