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Jeremy AppleyardSentom Pty Ltdwww.sentom.com
…Business Intelligence Leadership…
Oracle OLAP 10�
Oracle OLAP 10�A fully integrated Multi-dimensional
(OLAP) & Relational database platformfor Business Intelligence
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What is OLAP ?
PlanningAnalyticReporting
Ad-HocReporting
StandardReporting
Ad-hoc query
Time seriesShares/Indexes
Ad-hoc analysis
“Speed of thoughtAnalysis”
Advanced aggregationFinancial functionsCustom functions
“Analysis-freezone”
drill down slice & dice
rotate
PredictiveAnalysis
Statistical forecasting
Allocations
What-if modeling
SQLSQL –– Relational ServersRelational Servers
OLAP OLAP –– MultiMulti--Dimensional ServersDimensional Servers
+ + +
On-line Analytical Processing
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� Multi-dimensional models the business simply
� Rich & flexible reporting and analysis
� Speed of “thought” analysis
� What-if planning & analysis
� Predictive calculations
Why OLAP ?The obvious
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� Reporting at different levels
� Gaps in time series (Cartesian joins)
� Multi-Level stacked calculations
� Inter-Row calculations (self join)
� Dynamic nested ranking
� Faster combination queries (of all above)
Why OLAP ?The NOT SO obvious
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PlanningAnalyticReporting
Ad-HocReporting
Relational Multi-dimensional (OLAP)
Problem # 1Traditional response – divide by application
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Clients
MultidimensionalDatabases
OperationalSources
Data MartsData Warehouse
Runtime
Warehouse ETL
OLAP ETL
DB2, SQL Server,
Oracle, etc.
RelationalDatabases
2 versions of the truth
Problem # 2Traditional response – divide by database
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AdAd--hochocreportingreporting
PredictivePredictiveAnalysisAnalysis
Low endLow endAnalyticsAnalytics
StaticStaticStandardStandard
ParameterParameterReportingReporting
Data Data MiningMining
< Analysis Continuum >
High endHigh endAnalyticsAnalytics
Dashboards Dashboards and Portalsand Portals
AnalyticalAnalyticalApplicationsApplications
ExcelExcel
SQLSQL –– Relational ServersRelational Servers
OLAP OLAP –– MultiMulti--Dimensional ServersDimensional Servers
DM DM ServersServers
Organizations choose BI applications based on analytical power. Personal productivity tools are used to fill in any presentation gaps.
Problem # 3Traditional response – divide by analytical power
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EIS
PlanningBudgeting
Forecasting
Daily or Monthly Sales / Finance / Branch
reports
M/XM/X--OLAP OLAP Custom DevelopmentCustom DevelopmentLow data volume Low data volume
MOLAP ApplicationMOLAP ApplicationMedium data volumeMedium data volume
SQL Query SQL Query R/X R/X –– OLAPOLAPLargest Largest --> > user base & user base & data volumedata volume
Organisations choose BI applications based on end user characteristics & analytical power requirements
PredictivePredictive AnalysisAnalysis
High End High End Controlled Controlled AnalyAnalyticstics
Low (SQL) Low (SQL) High (OLAP)High (OLAP)End User End User AnalyAnalyticstics
Problem # 4Traditional response – divide by usage & volume
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Business Rules
Business Rules
Business RulesBusiness Rules
Business Rules
Business Rules
Business Rules
Business Rules
Business Rules
Business Rules
Problem # 5 = (1+2+3+4)x5 Traditional response – divide by geography + Excel
Profit = A+C+D-EEXCEL
Profit = C+D+E
Profit = Z-C+B
Profit = C+F+E
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“The Global BI Headache”
“As a BI Manager, I have to select from too many
technologies to make it all work”
“Resulting in Cost and Complexity”
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A Solution – Step 1 One Integrated Relational & Multi-Dimensional Server
Business Rules
Business Rules
Business Rules
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A Solution – Step 2 Make it Scalable & Secure
Business Rules
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� Single Open Language e.g. ANSI SQL
� Cross Relational & Multi-Dimensional access� Full range of analytical functions� Support Predictive analysis
� “Speed of thought” Performance� Scalability (Partitioning & Parallel query execution)� Manageability in a single server (Disaster/Recovery)
� Internet deployment � Single security model� Single meta data repository� Cost effective for enterprise wide solutions
A Solution – Step 3 Fully Integrate the technologies
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Oracle 10g OLAP
Oracle OLAP 10g
���� ���� ����
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What does it mean to be an integrated Relational-Multidimensional Database for
Business Intelligence?
Oracle OLAP 10g
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What has Oracle leveraged
+’s of Relational &
+’s of Multi-Dimensionalthen integrate
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� Supports large data volumes & concurrent usage� SQL is the most open & widely used language� Largest share of reporting and web based tools� Largest available skill base� High transactional performance � High security levels� Mature management /administration tools� Inbuilt disaster / recovery / logging capabilities
X Lacks ability, functionality & performance in expressing and executing complex analytical queries
X Unable to support planning, modeling, forecast and what-if
X At best RDBMS provide a partial OLAP solution
What has Oracle leveraged… SQL & RDBMs perspective
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What has Oracle leveraged… Multi-dimensional perspective
� Illustrates business models simply� Fast complex query execution� Predictive analysis
- models, forecasts, allocation, statistical & custom� Collaborative Analysis & Planning
X Lacks scalability
X Closed as SQL is not supported, propriety Front Ends
X Large volumes of data movement
X Latency issues surround extraction and data reloading
X Delays in timely information to business users
X Costs prohibit wide enterprise usage
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What has Oracle leveraged… Query characteristics
Relational Query Characteristics
� Any sample (where) and any shape (group by) � Extended result sets with ANSI standard SQL OLAP functions
Multi-Dimensional Query Characteristics
� Pre-defined sample & shape = CUBE “models the business”� Cube encapsulate all business rules� Dimensions are organised into hierarchies� Extending result set with Share, YTD’s, Variance, Period to Period
comparisons and combinations of all above is easy & fast � Beyond relational reporting to provide, budgeting, modelling, what-if
analysis and forecasting.
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Integrated RDBMS-MDDS
– Single RDBMS-MDDS process– Single data storage– Single metadata repository– Single set of management tools– Single security model– OLAP API and SQL interfaces (Business Objects Support)– Common look and feel front end– Query seamlessly from Multi-dimensional data into
relationally stored data.
Oracle 10G OLAP in the RDBMS
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Integrated RDBMS-MDDSDirect ETL and Single Meta Data
Clients
Data Warehouse
MultidimensionalData Types
RelationalData Types
SQL
Warehouse/OLAP ETL
Runtime
Sources
Data &Business Rules
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Integrated RDBMS-MDDS Grid Computing – Unique & Cost effective
Data Sources Real Application Cluster
Application
Servers
Shared Cache and
Disk
Highly scalable automatic “share all”
architecture
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Oracle OLAP 10g
“under the hood”
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Oracle 10g - Business Intelligence Platform
Oracle 10gOracle 10g
MOLAP <Analytical workspace (Express DB)<Provide open access via SQL<Multi-Write partition & parallel support<Extend predictive analysis functions
ROLAPIndexing performance >Summarization & management >
SQL OLAP functions improved query speed >
CWM Metadata
Insulate BI applications from physical layer > Implement a standard meta data layer >
Bi-BeansRepository
<XML based repository<Enable end-user sharing & collaboration
Java OLAPAPI
Enable true MD awareness >Intelligent caching >
Part query evaluation >
<Perform analytics closest to the data<Remove data movement for calculations
ETL <Scheduling<End to end Meta-data management
Warehouse Builder >Leverages database ETL functions >
Oracle ReportsOracle ReportsOracle DiscovererOracle Discoverer
Oracle Enterprise, Planning & BudgetingOracle Enterprise, Planning & Budgeting(extend & customize) Replaces OFA & OSA(extend & customize) Replaces OFA & OSA
Oracle PortalOracle Portal
BI Beans BI Beans ApplicationsApplications
BI-Beans +Jdeveloper
Views <Query equivalency<Seamless interaction
RDBMs views >Object functions >
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Oracle10g Storage Model
SALES
UNITS
COST
FORECAST_SALES
FORECAST_UNITS
AW$SALES
SALES
Oracle Data Files
Objects in an analytic workspace are stored in separate rows in an AW$ table.The AW$ table is stored as a BLOB in an Oracle Data file(s)
SQL> desc aw$salesName Null? Type------------------ -------- --------XTNUM NUMBER(8)AWLOB BLOB
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Oracle10g Storage Model
SALES
UNITS
COST
FORECAST_SALES
FORECAST_UNITS
AW$SALES
SALES
Oracle Data Files
AW$ table can be partitioned using table partitioning
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Partitioned Variables (Cubes)… increasing scalability & performance to a new level
� Engine level partitioning of variable objects in the analytic workspace
� Each partition becomes a row in the AW$ table
� Partitioning methods– RANGE – LIST– CONCAT
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� RANGE partitioning– Partitions based on a range of dimension
members
Customers less than 1000
Customers less than 2000
Customers less than 3000
Customers less than 4000
Customers less than 5000
Sales
Partitioned Variables (Cubes)… increasing scalability & performance to a new level
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� LIST partitioning– Partitions based on a list of named members
<‘JAN03’,’FEB03’,’MAR03’>
<‘APR03’,’MAY03’,’JUN03’>
<‘JUL03’,’AUG03’,SEP03’>
<‘OCT03’,’NOV03’,’DEC03’>
<‘JAN04’,’FEB04’,’MAR04’>
Sales
Partitioned Variables (Cubes)… increasing scalability & performance to a new level
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Average Response Times
Data Intechange - Rows per Second
0.00
5000.00
10000.00
15000.00
20000.00
25000.00
Insert into Oracle table Select data from Oracletable
Express Server 6.3
9i OLAP option
Data Interchange… more benefits from integration
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SQL Query Processing
APPLICATION
RELATIONAL ENGINE
SELECT Statement
OLAP_TABLE
Select list and WHEREclause predicates
Returns data inmultidimensional
format
Returns data inRow format
Returns data throughOCI or JDBC
MULTIDIMENSIONAL ENGINE
OLAP DML commands
Aggregation andcalculation
SQL filter evaluated here(new to 10g)
SQL filter evaluated here(9.2 and 10g)
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A Time Dimension View
create type time_type_row as object (month varchar(10),month_label varchar(6),quarter varchar(3),quarter_label varchar(8),year varchar(3),year_label varchar(5));
Step 1: Create an Abstract Data Type
create type time_type_table as table of time_type_row
Note: required in 9.2 but optional in 10g
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A Time Dimension View
create or replace view time_view asselect *from table(OLAP_TABLE(‘production.sales DURATION query',
'TIME_TYPE_TABLE','limit time to levelrel.time ''month''','DIMENSION month FROM timeMEASURE month_label FROM name.timeMEASURE quarter FROM time.qtrMEASURE quarter_label FROM time.qtr.nameMEASURE year FROM time.yearMEASURE year_label FROM time.year.name'));
Step 2: Create a View
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A Time Dimension View
SQL> desc time_view;Name Null? Type-------------------- -------- ----------------------------MONTH VARCHAR2(10)MONTH_LABEL VARCHAR2(6)QUARTER VARCHAR2(3)QUARTER_LABEL VARCHAR2(8)YEAR VARCHAR2(3)YEAR_LABEL VARCHAR2(5)
The view
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A Time Dimension View
SQL> select month, month_label, quarter, quarter_labelfrom time_viewwhere quarter = '77';
MONTH MONTH_ QUA QUARTER_---------- ------ --- --------49 Jan02 77 C-Q1.0250 Feb02 77 C-Q1.0251 Mar02 77 C-Q1.02
Query using standard SQL
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OLAP Expression in SQL
SELECT time_id, channel_id, product_id, customer_id, sales,
olap_expression(olap_calc, 'lagpct(sales,1,time)’)
pct_change_sales,
olap_expression(olap_calc, ‘sales/sales(product ‘TOTAL PRODUCT)’) product_share
FROM sales_view
WHERE time_parent = '2002'
AND channel_id = 'Total Channel'
AND product_type = 'CARS'
AND customer_parent = ‘AUSTRALIA';
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Aggregation SourcesTraditional Method
UNIT_PRICE at Item and Month levels
SALES at Item and Month levels SALES = UNITS_SOLD * UNIT_PRICE
AGGREGATE SALESUSING SALES_AGGMAP
Traditional method of storing data at leaf levels from the results of a computation
SALES at summary levels
UNITS at Item and Month levels
UNITS at summary levels
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Aggregation SourcesAGGREGATE_FROM
UNIT_PRICE at Item and Month levels SALES_FORMULA
SALES at summary levels
SALES_FORMULA eqUNITS_SOLD * UNIT_PRICE
Aggregate from SALES_FORMULA
Using AGGREGATE FROM to aggregate from a formulaeliminates ETL step and reduces size of database
UNITS at Item and Month levels
UNITS at summary levels
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Query Equivalency/Rewrite into OLAP… the ultimate integration
View with fact
detail
View with summary
dataSQL client
Analytic Workspace
1) Application queries summary data by issue SQL against detail fact table with GROUP BY
2) Query is automatically rewritten to use summary view
3) Data is accessed from analytic workspace
An OLAP cube is now just a summary table ( M V )
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Oracle OLAP 10gBusiness Intelligence Beans
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BI Front Ends…end user requirements
EIS
PlanningBudgeting
Forecasting
Daily or Monthly Sales / Finance / Branch
reports
Custom Custom DevelopmentDevelopment
ApplicationApplication& &
ability to extendability to extend
Reporting Reporting & &
Analysis Analysis
BI Applications are used by different users with individual requirements, needs and interfaces
High end controlled High end controlled AnalyAnalyticsticsEase of useEase of useKPI focus not tool usageKPI focus not tool usage
Low to high analytics Low to high analytics
Predictive analysisPredictive analysisWhatWhat--if modeling if modeling Budgeting & Forecasting Budgeting & Forecasting
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� Suitable
� Usability
� Secure Deployment
� Buy … but extendable
� Cost effective for enterprise wide solutions
� Objects managed in a single server
BI Front Ends…requirements perspective
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TableGraph
Query
Calc
List Sort
SaveBusiness IntelligenceBusiness Intelligence
ApplicationsApplications
All Business Intelligence Applications have the same core components …… so Oracle have built these core components in Java – BI Beans and used them across all BI applications
Oracle BI Beans …common components, single look and feel for all data
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Extend Plug
Oracle BI-BeansOPEN Analytic Architecture – front end building blocks
TableGraph
Query
Calc
List Sort
Save
Oracle Discoverer /
OLAP
Oracle BI Beans
Oracle Spreadsheet
Add-InOracle Reports
Enterprise Planning & Budgeting
Oracle Portal
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Oracle OLAP 10gSummary
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Oracle OLAP is the complete analytic data source to all BI applications
PlanningAd-Hoc / AnalyticReporting
StandardReporting
Oracle OLAP 10g
MultidimensionalData Types
RelationalData Types
Data &Business Rules
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Oracle Database is the only fullyintegrated Relational-Multidimensional Database for Business Intelligence
Single RDBMS-MDDS processSingle data storeSingle metadata repositorySingle set of management toolsSingle security modelOLAP API and SQL interfaces
OracleYesYesYesYesYesYes
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Oracle Business Intelligence PlatformInformation Matters
Oracle Discoverer
Oracle BI Beans
Oracle Spreadsheet
Add-InOracle Reports Third Party
Profit = A+C+D-E
Oracle Portal
“Reduces Cost and Complexity”
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Next Steps….� Recommended sessions
– Oracle BI Tools Vision– Analysing OLAP Cubes with Discoverer Drake
� Recommended demos and/or hands-on labs– Analytical Workspace Manager– Discoverer Drake – OLAP Excel Add-in
� See Your Business in Our Software– Visit the DEMO grounds for a customized architectural
review, see a customized demo with Solutions Factory, or receive a personalized proposal. Visit the DEMO grounds for more information.
� Relevant web sites to visit for more information– www.oracle.com/solutions/business_intelligence– www.sentom.com
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Reminder –please complete the OracleWorld online session survey
Thank you.
Q U E S T I O N SQ U E S T I O N SA N S W E R SA N S W E R S
…Business Intelligence Leadership…
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…Business Intelligence Leadership…
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