Download OLAP (Online Analytical Processing)

Ayyat IT Group
Murad Faridi
Muhammad Waqas
Salman Raza
Junaid Pervaiz
Roll NO#2492
Roll NO#2803
Roll NO#2473
Roll NO#2468
Instructor :- “Madam Sana Saeed”
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OLAP
(Online Analytical Processing)

Architecture

Characteristics

Relational OLAP

Multidimensional OLAP

ROLAP VS. MOLAP


HOLAP
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What Is Data Warehouse?
consolidates the information from different
data sources, enabling OLAP (online
analytical processing), to help decision
support.
 is maintained separately from an operational
database (which is used for OLTP – online
transaction processing).

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OLAP
(Online Analytical Processing)
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Multi-Tiered Architecture
Metadata
other
sources
Operational
DBs
Extract
Transform
Load
Refresh
Monitor
&
Integrator
Data
Warehouse
OLAP Server
Serve
Analysis
Query
Reports
Data mining
Data Marts
Data Sources
Data Storage
OLAP Engine Front-End Tools
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What is OLAP?



On-Line Analytical Processing
Information technology to help the
knowledge worker (executive, manager,
analyst) make faster and better
decisions.
OLAP is an element of decision
support systems
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OLAP


•
•
•
•
Create an advanced data analysis environment
that supports decision making, business
modeling and operation research activities.
Characteristics of OLAP
Use multidimensional data analysis technique
Provide advance database support
Provide easy-to-use end user interfaces.
Support client/server architecture.
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Two types of database activity
 OLTP
and OLAP
OLTP: On-Line Transaction Processing






Short transactions, both queries and updates
(e.g., update account balance, enroll in course)
Queries are simple
(e.g., find account balance, find grade in course)
Updates are frequent
(e.g., concert tickets, seat reservations, shopping
carts)
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OLAP: On-Line Analytical Processing
 Long transactions, usually complex queries



(e.g., all statistics about all sales, grouped by
dept and month)
“Data mining” operations
Infrequent updates
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OLTP Compared With OLAP
On Line Transaction
Processing – OLTP
– Maintain a database that


On Line Analytical
Processing - OLAP
– Use information in
database to guide
is an accurate model of
strategic decisions
some real-world enterprise
• Complex aggregation
• Short simple transactions
queries
• Relatively frequent updates
• Transactions access only a • Infrequent updates
• Transactions access a
small fraction of the
large fraction of the
database
database
4/29/2017
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RELATIONAL OLAP






Provides functionality by using relational databases and
relational query tools to store and analyze
multidimensional data.
Build on existing relational technologies and represents
extension to all those companies that already used
RDBMS
ROLAP adds the following extensions to traditional
RDBMS
Multidimensional data schema support within the
RDBMS
Data access language and query performance are
optimized for multidimensional data.
Support for very large data bases
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Multidimensional OLAP
MOLAP extends OLAP functionality to MDBMS
 Best suited to manage, store or analyze
multidimensional data.
 Proprietary techniques used in MDBMS.
 MDBMS and users visualize the stored data as a 3dimensional cube i.e data cube.
 MOLAP data bases are known to be much faster
than their ROLAP counter parts.
 Data cubes are held in memory called “cube cache”.

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What shape am I?

I have 6 flat square faces

I have 12 straight edges

I have 8 corners.
I am a …………?
Fantastic!
I am a cube!
ROLAP vs MOLAP
Characteristics
ROLAP
MOLAP
SCHEMA
Uses
star schema
Additional
dimensions can be
added dynamically
Uses
data cubes
Additional dimensions
require re-creation of
the data cube.
Database
size
Medium to large
Small to medium
Architecture Client/server
Client/server
Access
Limited to predefined
dimensions
Support
ad-hoc
requests
Unlimited
dimensions
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ROLAP vs MOLAP
Characteristics
ROLAP
MOLAP
Resources
High
Very high
Flexibility
High
Low
Scalability
High
Low
Speed
Good
Faster
with small
data sets
Average for
medium to large
data set
for small to
medium data sets
Average for large
data sets.
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Implementation of the OLAP Server
ROLAP: Relational OLAP – data is stored in
tables in relational database or extended
relational databases. They use an RDBMS to
manage the warehouse data and aggregations
using often a star schema.
• They support extensions to SQL.
Advantage: Scalable.
Disadvantage: No direct access to cells.

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Implementation of the OLAP Server

MOLAP:Multidimensional OLAP - implements the
multidimensional view by storing data in special
multidimensional data structures.
Advantage:Fast indexing to pre-computed aggregations.
Only values are stored.
Disadvantage: Not very scalable.
•
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Characteristics of OLAP


Fast - means that the system targeted to deliver most
responses
to user within about five second, with the simplest
analysis taking no more than one second and very few
taking more than 20 sec.
Share - means that the system implements all the
security requirements for confidentiality and, if multiple
write access is needed, concurrent update location at
an appropriated level not all applications need users to
write data back, but for the growing number that do,
the system should be able to handle multiple updates
in a timely, secure manner.
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
Analysis - means that the system can cope with any
business logic and statistical analysis that it relevant
for the application and the user, keep it easy enough
for the target user. Although some pre programming
may be needed we do not think it acceptable if all
application definitions have to be allow the user to
define new adhoc calculations as part of the analysis
and to report on the data in any desired way, without
having to program so we exclude products (like Oracle
Discoverer) that do not allow the user to define new
adhoc calculation as part of the analysis and to report
on the data in any desired product that do not allow
adequate end user oriented calculation flexibility.
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Multidimensional - is the key requirement.
OLAP system must provide a multidimensional
conceptual view of the data, including full
support for hierarchies, as this is certainly the
most logical way to analyze business and
organizations.
Information - are all of the data and derived
information needed? Wherever it is and
however much is relevant for the application.
We are measuring the capacity of various
products in terms of how much input data they
can handle, not how many gigabytes they take
to store it.
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HOLAP
HOLAP is the product of the
attempt to incorporate the best
features of MOLAP and ROLAP
into a single architecture.
HOLAP
This tool tried to bridge the technology gap
of both products by enabling access or use
to both multidimensional database (MDDB)
and Relational Database Management
System (RDBMS) data stores.
HOLAP
HOLAP systems stores larger quantities of
detailed data in the relational tables while
the aggregations are stored in the precalculated cubes.
HOLAP
HOLAP also has the capacity to “drill
through” from the cube down to the
relational tables for delineated data. Some
of the advantages of this system are better
scalability, quick data processing and
flexibility in accessing of data sources.
END
What appears to be the
end may really be a
new beginning.
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