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Data Modeling Techniques Using Oracle BI ServerMark Rittman, Director, Rittman Mead ConsultingOracle Open World 2008, San Francisco. Session #S300202
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Who Am I?
Oracle BI&W Architecture and Development Specialist
Co-Founder of Rittman Mead Consulting
Oracle BI&W Project Delivery Specialists
10+ years with Discoverer, OWB etc
Oracle ACE Director, ACE of the Year 2005Writer for OTN and Oracle Magazine
Longest-running Oracle blog
http://www.rittmanmead.com/blog
Chair of UKOUG BIRT SIG
Co-Chair of ODTUG BI&DW SIG
Speaker at IOUG and BIWA events
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Rittman Mead Consulting
Oracle BI&DW Project Specialists
Consulting, Training, Support
Works with you to ensure OBIEE andOracle BI Applications project success
Small, focused team
OWB, Oracle BI, DW technicalspecialists
Clients in the UK, Europe, USA
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What is this Presentation About?
For performance and data integration reasons, most mature OBIEE systems sourcetheir data from one (ormore) data warehouses
For initial implementations though, data is often initially accessed directly
OBIEE has some powerful features for creating integrated virtual logical models
For bringing together data from disparate systems (federation) For integrating historic and real-time data (fragmentation)
For creating calculations and derivations
For handling multi-valued dimensions
OBIEE also requires that any normalized data is converted into a star schema
These data modeling techniques, coupled with more formal data warehouses, can
allow you to deploy very flexible, agile data models to support your reportingThis presentation will take you through some of these data modeling techniques and will
discuss how these initial models are migrated to a more formal data warehouse
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Oracle Business Intelligence Enterprise Edition
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Data Access
Reporting data may be sourced frommultiple databases, applications
Oracle Database (including Oracle OLAP)
IBM DB2
Microsoft SQL Server Teradata
Microsoft Analysis Services (OLAP)
SAP BW 'info cubes'
SAP, Peoplesoft, Siebel, E-Business Suite
Oracle BI Server joins this source
data together and presents alogical business model
Facts
Dimensions
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Common Enterprise Information Model
Enables consistency, security, reuse, flexibility
Role-based views of the organization
Single metadata model of the business
Consistent definition of business measures, metrics, calculations
Drill paths, summaries, securityModel once, deploy anywhere
Across any data source
Model First development
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Data Modeling using Oracle BI Server
Design goal for the BI Server metadata layer is to create a Star Schema
Fact tables, Dimension tables
Drill paths
Source data can either be pre-integrated (data mart,
data warehouse) or integrated usingthe BI Server (federated)
Federated can be faster to deliver (report in place)
- Assumes data sources share common key values
Data marts and data warehouses are simpler and canbe faster to query
MDX (OLAP, Multi-dimensional) data is converted intoRDBMS row-sets
In this presentation, we will look at how you can use thedata modeling features of OBIEE to create logical star schemas
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Sample Business Model
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Data Modeling Workflow Steps
1.Define logical model in BI Administrator tool
2. Import physical data sources & define primary and foreign keys
3. Map logical model to physical model
4. Create initial calculations
5. Create dimensions6. Create initial presentation model
7. Create complex calculations, time dimensions etc
8. Add additional columns to presentation model
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Data Modeling Scenario
Global Company has a data warehouse, containing order and customer data
Requirement is to take the existing warehouse (normalized)and turn it into a star schema (denormalized)
For now, OBIEE will be used to map the data
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Step 1: Create Logical Layer
Firstly, create new logical model
Then create logical tables and columns
Either create from scratch, then connect to physical(best practice)
Or define by dragging and dropping physical tablesStart from fact table and work outward to the dimensions
Do one fact/dimension combination first
Then add more facts
... and more dimensions
Add details on datatypes etc later on
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Logical Join Types
Logical tables are by default not joined
Logical layer supports both foreign keyand complex joins
Foreign key joins not recommended
Restrict choices that BI servercan make over join paths
Only there for backward compatibility
Complex joins are preferred
Indicates relationship betweentables
Allows BI Server to determinebest join path
- Useful when more than oneLogical Table Source
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Step 2: Create Physical Layer
Define ODBC connection from client or server to database
ODBC only needed to define initial connection, import metadata
Native connection (OCI etc) takes over from then
Import tables, views, keys etc from
data sourceDefine keys, foreign keys if needed
Update row counts
Define table aliases
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Physical Join Types
If source data has foreign key joins, they will be imported
If not, they can be defined in the physical model
Two types of join
Foreign key joins (e.g. CUSTID = CUSTID)
- 1:N joins onlyComplex joins (TRAN_DATE between
STARTDATEID and ENDDATEID)
- also supports 1:1, O:1, 1:O etc
Important to create physical joinsbefore importing in to logical layer
Logical joins rely on physical joinsto define paths
Makes life a lot simpler
Create joins using Join Manager,
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Step 3: Mapping the Logical Model to the Physical Model(s)
Once you have your logical model and one or more physical models, you can map themtogether
The simpler the mapping, the better
Drag and drop physical columnson to the logical model
Logical model will inheritphysical data types
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Logical Table Sources
Logical tables can have more than one physical source
Can create >1 Logical Table Source
BI Server creates two physical queries
Useful when mapping data from
different levels and when using fragmentation
Each Logical table source can have>1 table mapped
BI server creates one query per LTS, joiningas per physical foreign key joins
Used when you need columns from otherjoined tables in your logical table(join only happens if additionalcolumns requested)
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Consider Where to Join Data
Again, consider where to integrate and join data
Whilst the BI Server can join data, it may be faster to load into a data mart
ETL tools can integrate data at time of load
Denormalized data structures can be faster than joining at report time
Indexing, Summary Management, Query OptimizationCreating a data warehouse still is the optimal solution
BIEE data source joining features are most appropriate when bringingnew data in thats not yet in the data warehouse
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Step 4: Create Initial Calculations
Calculations are created as additional logical columns
Sit alongside existing logical columns derived from physical layer
Calculations can alsobe created within report
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OBIEE SQL and the Expression Editor
Calculations within the repository are defined using the Expression Editor
Uses OBIEE SQL Syntax
Reference functions,expressions, logicalcolumns, constants,variables
Same SQL syntax acrossall supported databases
Ability to pass-throughfunction calls usingEVALUATE function
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Step 5: Create Dimensions and Hierarchies
Dimensions define hierarchical relationships between columns
Also used when defining level-based measuresand summaries
Level-based, starts with Grand Total level
One-way export out to Oracle dimensions (DDL)also possible
Enables hyperlinks on columns withindimension levels
Drill-down through the hierarchy
No drill-up or drill to related (yet)
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Step 6: Create Initial Presentation Models
Business models themselves are not visible to users
Users see presentation models whose columns aredefined in business models.
Appear as subject areasin the Oracle BI Answers
Appear as databases toclient tools using ODBC
Can create multiplePresentation Catalogs fromsingle Logical Model
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Step 7: Creating Time Dimension and Time-Series Calculations
OBIEE 10.1.3.2 supports limited time-series functions
AGO value of a measure x months, quarters, years ago
AGO(Videostore."Sales fact view".Sales, Videostore.TimesDim."Month", 1)
TODATE value of a measure to the end of a month, quarter, year
AGO(Videostore."Sales fact view".Sales, Videostore.TimesDim."Quarter", 1)
Requires at least one time dimension in themetadata layer
Requires at least one or more Chronological keys
Monotonically increasing value (DAY etc)
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DemonstrationCreating the Initial Common Enterprise Information Model
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The Calculation Wizard
Wizard that generates share, percentage change etc calculations
Useful in conjunction withTime Dimension
% Change since last month
Change since last month etc
Index
Percent
Wraps ISNULL around calcs
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Reporting Across Multiple Data Sources
OBIEE can combine and join data from multiple data sources
Data is retrieved in separate SQL, MDX calls and joined using the BI Server
Allows you to perform federated queries
Watch out for performance though
End goal should be to eventually move reporting data to a data warehouse
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Federated Query Scenario
Additional data exists in an Excel spreadsheet
Contains quotas, plus information on products and dates
Quotas are at the product category and month level
Existing data is at the product and day level
Requirement is to join data, but only show quotas the relevant level
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Joining A Second Data Source : Step 1
Import the new schema into the physical model, ensure FK and PKconstraints are set
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Joining A Second Data Source : Step 2
Use the Join Manager, or Physical Model Diagrammer, to createjoins between tables in the two schemas
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Joining A Second Data Source : Step 3
Add the new dimension tables as additional logical table sourcesto the exisiting logical dimension tables
Set the dimension hierarchy level at which theyare applicable
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Joining A Second Data Source : Step 4
Add the additional measures to the fact table
Set the dimension levels at which the measures become relevant
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DemonstrationCreating Federated Data Sources
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Incorporating Real-Time Data Using Fragmentation
Current data source for OBIEE might be a data warehouse
Data loaded once a day, once a week or with some other time lag
You may wish to bring real-time data into the OBIEE environment
One option is to load this data into the warehouse in real-time
Another is to use the fragmentation feature to time-partition your measures
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Fragmentation
Logical tables have >1 Logical Table Source
Logical Table Source 1 = Historical Data
Logical Table Source 2 = Real-Time Data
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Advanced Data Modeling Issues
In real-life implementations, the source data may present some challenges
Physical to Logical Modeling Issues
Circular joins
Fan traps
Dimensional Modeling issues Fact to dimension many-to-many relationships
Ragged and unbalanced hierarchies
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Physical to Logical Modeling Issues : Circular Joins
Data in your physical source system may use circular joins
Valid in an OLTP system, and in tools such as Discoverer
Causes issues in tools such as OBIEE as join path cannot be circular
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Circular Joins Solution : Alias Tables
In this example, dimension information is held in three tables
Employees, departments, regions
Employees doubles-up as a table that provides details on regional managers as well
Solution is to create an alias for the employee table and join this to region instead
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Fan Trap Queries
Caused when one master table (i.e. a dimension) joins to two fact tables (sales, budget)
You would expect the aggregations on each table to be correct, however regularSQL queries can over-count totals due to cartesian joins forming
Known in the Discoverer world as fan trap queries
SQL> select a.acc_name,2 sum(sales),3 sum(budget)4 from fan_trap_accounts a, fan_trap_sales s, fan_trap_budget b5 where a.acc_id = s.acc_id6 and a.acc_id = b.acc_id7 group by a.acc_name8 order by a.acc_name9 /
ACC_NAME SUM(SALES) SUM(BUDGET)-------------------- ---------- ----------- ACCOUNT1 900 1050
ACCOUNT2 130 200 ACCOUNT3 600 750 ACCOUNT4 600 600
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Automatic Fan Trap Query Resolution
The Oracle BI Server automatically detects fan trap queries, and generates twoseparate SQL queries which it combines at the BI Server level
Solves issue, functionally identical, but not quite as efficient as Discoverer
Two SQL queries vs. one, and join done in BI Server memory
-------------------- Sending query to database named ora11g (id: ):
select T6678.ACC_NAME as c1,sum(T6685.SALES) as c2
fromCUST_ORDER_HISTORY.FAN_TRAP_ACCOUNTS T6678,CUST_ORDER_HISTORY.FAN_TRAP_SALES T6685
where ( T6678.ACC_ID = T6685.ACC_ID )group by T6678.ACC_NAMEorder by c1
+++Administrator:2a0000:2a0001:----2008/08/26 17:24:48
-------------------- Sending query to database named ora11g (id: ):
select T6678.ACC_NAME as c1,sum(T6681.BUDGET) as c2
fromCUST_ORDER_HISTORY.FAN_TRAP_ACCOUNTS T6678,CUST_ORDER_HISTORY.FAN_TRAP_BUDGET T6681
where ( T6678.ACC_ID = T6681.ACC_ID )group by T6678.ACC_NAMEorder by c1
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Bridge Tables
In some cases, there might exist a many-to-many relationship between a fact tableand a dimension table
Patient diagnoses, sales made by salespeople
In entity relationship modeling, you would resolvethis many-to-many with and intersection table
The intersection in dimensional modeling termsis called a bridge table
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Bridge Tables and Dependent Dimensions
Marking a dimension table as a bridge table stops OBIEE thinking of it as a fact table
Any dimensions that join to it will not now join to the main fact table
To avoid metadata consistency issues, add the dimension table to the bridge LTSand then add any dimension columns to the bridge logical table
Remove the dimension table from the logical model, use the bridge table instead
Dimension data can then be accessed as required
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DemonstrationUsing Bridge Tables
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Ragged Hierarchies
Some hierarchies are unbalanced or ragged
Do not have the same number of levels downeach hierarchy path
Typically found in Organization, Sales Team hierarchies
Stored in the source system in a parent-child form
OBIEE requires level-based hierarchies
Three main methods of resolving this
Flatten the hierarchy
Use stack/descendent tables
- For full solution, see the URL below
Use Essbase as a data source
Organization
Mark Jon Pete Stan
James Bill Alex
Arthur
Sarah Phil
Darren
Stacey
Gavin
Sarah
http://www.rittmanmead.com/2007/06/21/obiee-data-modeling-tips-3-ragged-hierarchies/
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Flattening Ragged Hierarchies
OBIEE requires (in 10gR3 release) all hierarchies to be level-based
Most common solution then is to take the parent-child hierarchy, determine themaximum depth, and create a flattened version of it
Where a hierarchy path is missing descendants, next highest member is copied down
Works well but needs rebuilding if the hierarchy increases in depth
Essbase physical value-based and ragged hierarchies are supported, but are thenconverted to this flattened format
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Summary
The data modeling features of OBIEE are a useful way to create an on-the-fly logicaldata model for your reporting
It is possible to turn a 3NF model into a star schema
Integrate data from multiple source (federation)
Integrate historic and real-time data (fragmentation)
Create calculations and report drill-paths
This is often a useful first step when prototyping your system
Over time, migrate the data you are reporting on to a formal data warehouse
Use the modeling features of the OBIEE suite to bring in data from all your sources
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8/9/2019 OBIEE buenas practicas
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T : +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Further Reading
Other Rittman Mead sessions
#S300201 : Automating Business Decisions with Oracle Real-Time Decisions
#S300202 : Data Modeling Techniques using Oracle BI Server
#S301063 : Extending and Customizing the Oracle BI Applications Data Warehouse
#S301081 : ODTUG Essbase Symposium Part 3 : Oracle Business IntelligenceEnterprise Edition
Visit our website for presentations, papers,the Rittman Mead Blog
http://www.rittmanmead.com
Contact me at [email protected]
Thank you for attending, please dont forget tocomplete the online evaluation
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8/9/2019 OBIEE buenas practicas
49/50
-
8/9/2019 OBIEE buenas practicas
50/50
T : +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Data Modeling Techniques Using Oracle BI ServerMark Rittman, Director, Rittman Mead ConsultingOracle Open World 2008, San Francisco. Session #S300202
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