2. 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.
4. TTTTeradataeradataeradataeradata
Data Analytics
• OLAP
• Data Mart
• Data Mining
• Active Intelligence
Teradata became more common in
corporations where enterprise-wide detail data
may be used in on-line analytical processing to
make strategic and tactical business decisions.
5. OOOOLAPLAPLAPLAP
• 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 trends
may 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 and
purpose, they often grow quite large as their utility becomes more fully exploited by the
enterprise.
• Data Warehousing is a process, not a product. It is a technique to properly assemble and
manage data from various sources to answer business questions not previously possible or
known.
6. DDDDataataataata MMMMartartartart
A data mart is a special purpose subset of
enterprise data used by a particular
department, function or application.
Collection of some specific data from a
huge data.
For Ex: Suppose I have a table which
contains whole India’s Rice production
details
If my intention is Only Maharastra data,
then I can have a small table which can only
contain Maharastra data.
Data Mart is of 3 types
1. Physical
a) Dependent
b) Independent
2. Logical(View)
Production Table of whole
India
Production Table
of only MH
Data
File
Independent
Dependent
View for
Maharastra Data
12. DDDDataataataata WWWWarehousearehousearehousearehouse vsvsvsvs DDDDataataataata MMMMartartartart
Data Warehouse:
•Holds multiple subject areas
•Holds very detailed information
•Works to integrate all data sources
•Does not necessarily use a dimensional
model but feeds dimensional models.
Data Mart
•Often holds only one subject area- for
example, Finance, or Sales
•May hold more summarized data (although
many hold full detail)
•Concentrates on integrating information from
a given subject area or set of source systems
•Is built focused on a dimensional model using
a star schema.
13. DDDDataataataata MMMMiningininginingining
• Data Mining is an analytic process designed to
explore data (usually large amounts of data -
typically business or market related - also known as
"big data") in search of consistent patterns and/or
systematic relationships between variables, and
then to validate the findings by applying the
detected 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 with
data preparation which may involve cleaning data,
data transformations, selecting subsets of records
(2) model building or pattern identification with
validation/verification,
(3) deployment (i.e., the application of the model to
new data in order to generate predictions).
14. AAAActivectivectivective IIIIntelligencentelligencentelligencentelligence
• 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.
15. PPPPassiveassiveassiveassive IIIIntelligence vsntelligence vsntelligence vsntelligence vs AAAActivectivectivective IIIIntelligencentelligencentelligencentelligence
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.
16. OLAP
AAAActivectivectivective DDDDataataataata wwwwarehousearehousearehousearehouse
Depending upon the freshness of data, data warehouse is
of two types.
• Active data warehouse
• We need fresh data to do active event for taking
decision at run time.
• ADW is the timely, integrated, logically consistent
store of detailed data available for tactical and
strategic driven business decisions.
• Example: Provide discount coupons at Casino
according to the Roller’s success or failure.
• Passive Data warehouse
Operational database
(OLTP)
Accurate and up to minute data
OLAP
Operational database
(OLTP)
Hourly/Daily/Weekly/Monthly
