Olap

04/08/23 Sudarshan 1

OLAPOLAP

(Online Analytical Processing)(Online Analytical Processing) ArchitectureArchitecture

CharacteristicsCharacteristics

Relational OLAPRelational OLAP

Multidimensional OLAPMultidimensional OLAP

ROLAP VS. MOLAP ROLAP VS. MOLAP


What Is Data Warehouse?What Is Data Warehouse? consolidates consolidates the information from different the information from different

data sources, enabling OLAP (online data sources, enabling OLAP (online analytical processing), to help decision analytical processing), to help decision support.support.

is maintained is maintained separately separately from an operational from an operational database (which is used for OLTP – online database (which is used for OLTP – online transaction processing).transaction processing).


OLAPOLAP((Online Analytical ProcessingOnline Analytical Processing))


Multi-Tiered ArchitectureMulti-Tiered Architecture

DataWarehouse

ExtractTransformLoadRefresh

OLAP Engine

AnalysisQueryReportsData mining

Monitor&

IntegratorMetadata

Data Sources Front-End Tools

Serve

Data Marts

Operational DBs

other

sources

Data Storage

OLAP Server


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


OLAPOLAP Create an advanced data analysis environment Create an advanced data analysis environment

that supports decision making, business that supports decision making, business modeling and operation research activities.modeling and operation research activities.

Characteristics of OLAPCharacteristics of OLAP• Use multidimensional data analysis techniqueUse multidimensional data analysis technique• Provide advance database supportProvide advance database support• Provide easy-to-use end user interfaces.Provide easy-to-use end user interfaces.• Support client/server architecture.Support client/server architecture.


Two types of database activity OLTP and OLAPOLTP: 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)


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


OLTP Compared With OLAPOLTP Compared With OLAP

On Line On Line TransactionTransaction Processing – OLTPProcessing – OLTP

– – Maintain a database that Maintain a database that is an accurate model of is an accurate model of some real-world enterprisesome real-world enterprise

• • Short simple transactionsShort simple transactions

• • Relatively frequent updatesRelatively frequent updates

• • Transactions access only a Transactions access only a small fraction of the small fraction of the databasedatabase

On Line On Line Analytical Analytical Processing - OLAPProcessing - OLAP

– – Use information in Use information in database to guide database to guide strategic decisionsstrategic decisions

• • Complex aggregation Complex aggregation queriesqueries

• • Infrequent updatesInfrequent updates

• • Transactions access a Transactions access a large fraction of the large fraction of the databasedatabase



RELATIONAL OLAPRELATIONAL OLAP Provides functionality by using relational databases and Provides functionality by using relational databases and

relational query tools to store and analyze relational query tools to store and analyze multidimensional data.multidimensional data.

Build on existing relational technologies and represents Build on existing relational technologies and represents extension to all those companies that already used extension to all those companies that already used RDBMSRDBMS

ROLAP adds the following extensions to traditional ROLAP adds the following extensions to traditional RDBMSRDBMS

Multidimensional data schema support within the Multidimensional data schema support within the RDBMS RDBMS

Data access language and query performance are Data access language and query performance are optimized for multidimensional data.optimized for multidimensional data.

Support for very large data basesSupport for very large data bases


Multidimensional OLAPMultidimensional OLAP MOLAP extends OLAP functionality to MDBMS MOLAP extends OLAP functionality to MDBMS Best suited to manage, store or analyze Best suited to manage, store or analyze

multidimensional data.multidimensional data. Proprietary techniques used in MDBMS.Proprietary techniques used in MDBMS. MDBMS and users visualize the stored data as a 3-MDBMS and users visualize the stored data as a 3-

dimensional cube i.e data cube.dimensional cube i.e data cube. MOLAP data bases are known to be much faster MOLAP data bases are known to be much faster

than their ROLAP counter parts.than their ROLAP counter parts. Data cubes are held in memory called “cube cache”.Data cubes are held in memory called “cube cache”.


ROLAP vs MOLAPROLAP vs MOLAPCharacteristicsCharacteristics ROLAP ROLAP MOLAPMOLAP

SCHEMASCHEMA Uses star schemaUses star schemaAdditional Additional dimensions can be dimensions can be added dynamicallyadded dynamically

Uses data cubesUses data cubesAdditional dimensions Additional dimensions require re-creation of require re-creation of the data cube.the data cube.

Database Database sizesize

Medium to largeMedium to large Small to mediumSmall to medium

ArchitectureArchitecture Client/serverClient/server Client/serverClient/server

AccessAccess Support ad-hoc Support ad-hoc requestsrequestsUnlimited Unlimited dimensionsdimensions

Limited to predefined Limited to predefined dimensionsdimensions


ROLAP vs MOLAPROLAP vs MOLAPCharacteristicsCharacteristics ROLAP ROLAP MOLAPMOLAP

Resources Resources HighHigh Very highVery high

FlexibilityFlexibility HighHigh LowLow

ScalabilityScalability HighHigh LowLow

SpeedSpeed Good with small Good with small data setsdata setsAverage for Average for medium to large medium to large data setdata set

Faster for small to Faster for small to medium data setsmedium data setsAverage for large Average for large data sets.data sets.


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.


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.•


Characteristics of OLAPCharacteristics 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.


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.


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.


What appears to be What appears to be the end may really the end may really be a new beginning.be a new beginning.

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