Dimensional modelingowb11gr2 presentation

WELCOME Dimensional Modeling
with Oracle Warehouse
Builder 11.2.
Maren Escherman

BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN

2011 © Trivadis
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AGENDA

1. Introduction
 Dimensional modeling
 From the dimensional model to a relational star schema
2. Oracle Warehouse Builder: Dimensional Features
 How to build a data mart
3. ETL Accelerators
 Loading dimensions and cubes
 Slowly Changing Dimensions
 Invalid data (Orphan Management)
4. Performance Optimization
5. Summary

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Oracle’s Data Warehouse Reference Architecture

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Introduction – Dimensional Modeling

 Design technique proposed by Ralph Kimball
 became the predominant approach to designing
data marts
 predictable and cost effective
 Can be used for any physical form
 relational or multidimensional
 Understandability
 easier to understand and more intuitive to query
 Performance
 optimized for data querying

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Dimensional Modeling 101

 Dimensional modeling always uses the concepts of facts (measures),
and dimensions (context).
 Dimensions are
 groups of hierarchies and
 the foundation of the fact table.
 Examples for Dimensions are
 Time (when)
 Customer (by whom)
 Geography (where)
 Product (what)
 Facts are additive figures such as quantity or cost per unit.

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Semantic Loss

Relational Star Schema:
Zeit Tables, Columns
Produkt

Kalender_Hierarchy
Product_Hierarchy

Dimensional Model:
Dimensions, Levels,
Hierarchies, Facts, …

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Avoid Semantic Loss

Relational Star Schema:
Zeit Tables, Columns
Produkt automatically created by OWB

Kalender_Hierarchy
Product_Hierarchy

Dimensional Model:
Dimensions, Levels,
Hierarchies, Cubes, …

OWB knows both worlds…
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Dimensional Modeling Using OWB

 Define, deploy, and load dimensional objects
 explicitly separates dimensional design from physical implementation
 Support for ROLAP and MOLAP

 ETL accelerators: dimension and cube operator
 New 11g R2 features:
 Automated orphan management policies for
loading dimensional objects
 Oracle OLAP Cube-Organized Materialized
Views support

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Dimensional Objects

 Easy to define by using dimension and cube wizard
 Additional OWB Meta-Objects are created
automatically (tables, sequences, bitmap
indexes, constraints, …)
 Implementation of best practices

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Dimension Tables

 Table <DIM_NAME>_TAB
 Primary Key on DIMENSION_KEY
 For each level
 Group of columns
 Index on business key
 Manual vs. Automatic binding
 Automatic binding
- is easy, but …
- … overwrites ALL manual modifications
on the dimension table
 Manual Binding allows you to follow
your own naming conventions and design
patterns

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«Solved» Dimensions

 “Solved “ dimensions contain records for higher level elements (control
rows)
 enable you to link fact data to a dimension at any level
 Control Rows have
 Negative surrogate Keys
 NULL attributes for lower levels

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Time Dimension

 Time dimension is not «solved»
 use 01.01.2011 for referencing «January 2011»
 Primary Key is a DATE attribute
 was a NUMBER in pre-11.2 releases
 allows partitioning of fact table by date
 Mapping to load the time dimension table is generated by OWB
 Different hierarchies are supported
 week hierarchy
 fiscal year hierarchy

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Fact Table

 Table <CUBE_NAME>_TAB
 One column per
 measure
 referenced dimension
 Bitmap Index and foreign key for each dimension attribute
 Unique key for the combination of dimension attributes (optional)
 No partitioning! No compression!

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Deployment

Design Repository Target Schema

Deploy Aggregation Materialized
Cube
Views

Dimension
Dimension
Deploy Data Objects
View

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Dimension/Cube Operator

 Used for loading data into a dimension/cube
 can do a lot of tedious work for you
 Are actually pluggable mappings
 Dimension Operator
 populates dimension surrogate key
 realizes SCD (Slowly Changing Dimensions) updates
 handles invalid dimension records
 Cube Operator
 looks up surrogate keys based on business key
 handles slowly changing dimensions
 handles invalid cube records

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Dimension Operator

 Supports two loading types:
 Load
 Remove
 Allows deduplication of level
elements
 Creates OWB$TEMP Tables
when mapping is deployed
 needed to provide some of the
hierarchy management and
loading functionality

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Slowly Changing Dimension

 Support for SCD Types 1, 2 and 3
 Type 1: Do not store history
 Type 2: Store the complete
change history
 Type 3: Store only the previous
value
 User can specify which attributes
trigger a new version
 only use from 10.2.0.3 upwards
 bugs in earlier versions

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SCD: «Under The Hood»

 Income_Level and Marital_Status trigger a new version
 For all other records only the most current record is updated

INCOME FIRST LAST MARITAL JOB VONDAT BISDAT
B Constantin Welles single Teacher 05-SEP-07 12-OCT-09
C Constantin Welles single Teacher 12-OCT-09 17-AUG-10
C Constantin Welles married Policeman 17-AUG-10 31-DEC-99

5-Sept-07 – Constantin Welles becomes customer.
12-Okt-09 – Constantin Welles gets a raise.
17-Aug-10– Constantin Welles marries.
??? – Constantin Welles has a new job.

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SCD: «Under The Hood»

 Kimball Design Tip #15: Combining SCD Techniques
 “For all prior dimension rows for a given customer, the current roll-up attribute
will be overwritten to reflect the current state of the world.”
 Kimball’s «Hybrid SCD» is not implemented by dimension operator

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Orphan Management

 What is an «orphan»?
 Dimension level element with no or invalid parent level attribute
 Fact with no or invalid dimension reference
 Available for dimensions and cubes
 Error tables store any records that are detected as anomalous.

Specify values used by
the default record

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Dimension Operator: «Behind the Scenes» - 1

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Cube Operator

 Loading Type
 INSERT LOAD
 LOAD
- source data is merged into
the cube
 REMOVE
 Enable Source Aggregation
 Handles slowly changing
dimensions
 Orphan Managment

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Cube Operator: «Behind the Scenes»

Source Aggregation

Dimension Surrogate
Key Lookup Orphan Management

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Features, Features, Features, …

 Loading dimensions
 Physical and logical deletes
 Dimensions without surrogate key
 History Logging Policy
 Support Multiple History Loading
 Out of Order History Loading
 Standard DML Error Logging
 Hierarchy Versioning
- Attribute changes are also recorded for higher levels
 Loading cubes
 Degenerate dimensions
 Different source aggregation functions (SUM, AVG, …)
 Different update functions (+=, -=, …)

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It’s all about performance!

 OWB can create
 Relational MViews
 Cube Based Mviews
 Mviews are created when deploying aggregation of cube
 MViews are no meta objects in OWB
 Relational Mviews are recreated every time you deploy
 MView Features like «fast refreshing» and «partition change tracking» are not
supported
 Hint: Select precompute for the leaf level also to avoid OLAP Bug.

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Licenses

 You need
 Data Integrator Enterprise Edition (formerly Warehouse Builder Enterprise ETL)
for
- SCD 2 and 3 support
- Orphan management
 OLAP Option for
- Creation of Materialized Views

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Be aware of …

 You can define default values without orphan management.
 Degenerate dimensions are possible (with 11.2)
 Each cube must have at least one measure
 For factless fact tables use one «dummy» fact with the default value 1
 Either all dimensions levels have a surrogate key or none
 Dimensional operators can also be used as source operators.

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Everything has to fit together

Dimension
Dimension Configuration
Settings

Cube
Configuration Operator
Properties
Cube
Settings

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Open the toolbox!

 Take everything that suits your
project-specific requirements
 You should have good reasons not to
use dimensional operators
 Model dimensions and cubes to avoid
semantic loss
 Do prototyping!
 Learn how things work
 Good understanding of the underlying
technologies (e.g. OLAP) is necessary
 With or without dimensional operators:
Use the same design pattern everywhere!!

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Summary

 Oracle Warehouse Builder offers a broad range of support for
dimensional objects.
 When using these features you …
 … can efficiently implement your ETL processes
 … are «production-ready» in a shorter time span
 … avoid semantic loss
 … achieve high quality in your implementation
 … have a standardized design pattern in your dimension and cube loading
mappings

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THANK YOU.
Trivadis AG

Maren Eschermann

Europa-Strasse 5
8152 Glattbrugg

Tel. +41-44-808 70 20
Fax +41-44-808 70 21

info@trivadis.com
www.trivadis.com

BASEL BERN LAUSANNE ZÜRICH DÜSSELDORF FRANKFURT A.M. FREIBURG I.BR. HAMBURG MÜNCHEN STUTTGART WIEN

2011 © Trivadis
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Dimensional modelingowb11gr2 presentation

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