Transcript Corporate PPT Template

Carey Probst
Technical Director
Technology Business Unit - OLAP
Oracle Corporation
3 Days: Raw Data to OLAP
Session: 40206
A Practical Approach for Rapidly
Delivering Successful OLAP Solutions
Challenge
 Deliver fully-functional OLAP solution in 3 days
 Keys to Success:
–
–
–
–
Existing, populated data source
Well-defined scope of data destined for OLAP
A little knowledge of Oracle Warehouse Builder
A preference for a reporting interface
How Is This Possible?
Key points:
 Oracle moved the OLAP engine into the database
–
–
No need to extract & reload data
Leverage existing designs for OLAP
 Automation of complex OLAP design tasks with
Oracle Warehouse Builder
–
Build, Deploy, Load & Query data
 Provide several methods of access
–
–
–
SQL access to OLAP
Allowing mix-and-match of OLAP and Relational
Support for multiple access tools & technologies
OLAP Architecture & Terminology
Oracle Database
OLAP API
Oracle Call Interface
JDBC
Relational Technology
Object Technology
OLAP Technology
SQL Engine
Table Functions
Multidimensional
Engine
Relational
Cubes
Multidimensional
Cubes
Step 1: Populated Data Store
 Constructing OLAP solutions from warehouses
is simple if
–
–
–
–
–
–
Dimensions have been identified
Hierarchies have been identified
Measures are known
Mapping to star schema is simplified
Mapping to OLAP structures streamlined
Many traditionally difficult queries can be easily
solved by OLAP
Step 2: Defined Scope of Data
 Know what data should logically be
summarized for queries
 Know types of queries users will want to ask
 Known level of aggregation
 Known data transformations required
Step 3: Oracle Warehouse Builder
 Oracle Warehouse Builder streamlines many
complex tasks of building OLAP solution
–
Design & Metadata population
–
Deployment of OLAP Cubes
–
Loading of Data
OLAP Design
OLAP Design – Best Practices
 Best Practice Guidelines
 Long and Short Descriptions Dimension Attributes:
–
Level Attribute mapped to column with name suffixed by
‘_LONG_NAME’ or ‘_SHORT_NAME’
 Time Dimension Descriptors:
–
–
–
–
Table name suffixed with ‘_TIME’
Level Attribute with column suffix ‘_END_DATE’
Level Attribute with column suffix ‘_TIME_SPAN’
Level names suffixed with _DAY, _MONTH etc..
OLAP Design - Dimensions
Metadata Design - Cubes
OLAP Deployment
OLAP Deployment
 Deploy scripts using the Deployment Manager
 Deploy OLAP metadata via OLAP bridge:
–
–
–
–
Creates all skeleton objects (empty)
Registered the objects in the OLAP catalog
Binds the OLAP objects to the relational objects
BI Beans enabled environment
 Creates a ROLAP environment
OLAP Deployment
OWB
Generate
Oracle Database
Scripts
OLAP
metadata
Bridge
OLAP catalog metadata
Register
Register
PL/SQL
DDL
Deploy
Relational Views
Publish
Tables,
Dimensions,
PLSQL etc..
Analytic Workspace
Create
OLAP Deployment - Bridge
OLAP Data Loading
OLAP Data Loading
 Load relational objects via a normal mapping
 Load the OLAP Analytic Workspace
–
Methods:
 Mapping – post mapping process
 Process Flow activity
–
–
Refresh or Insert into Dimensions
Refresh or Insert into Cubes
 Using an OWB wrapper procedure on top of
the RDBMS PL/SQL
OLAP Data Loading
Oracle Database
OLAP catalog metadata
Registered
Relational Views
Sources
Insert/Update
Cubes,
Dimensions,
Tables
Publish
Analytic Workspace
Load/Refresh
Step 4: Reporting Choices
Currently OLAP access is provided through:
 BI Beans
–
–
The Java query components to enable OLAP
Enables custom application development with several
deployment options
 Discoverer
–
–
The Ad-Hoc query tool now utilizing OLAP
OWB capable of generating Business Areas for Discoverer
 SQL
–
Analytic Workspaces can be queried through SQL
 OLAP Worksheet in OWB
–
Provides visualization during design & build iterations
Ongoing – Maintenance




Add new measures
Add new dimensions or hierarchies
Modify existing hierarchies
Add self calculating measures (formulas)
Add new stored measures
 Use Analytic Workspace Manager to define
the stored measure
 Run add_stored_measure utility to add to an
existing Standard Format (SF) cube
 Modify SQL views if using SQL queries
 Re-run AW enablement for BI Beans if using
CWM2 metadata
Add new dimensions or
hierarchies






Create new hierarchy
Add descriptions of hierarcy
Populate parent relationship.
Run groupingid to set new hierarchy details
Run hierheight to set new level details
Re-run AW enablement for BI Beans if using
CWM2 metadata
Modify existing hierarchies
 Make changes to parents, levels, etc. as
required.
 Run groupingid to set new hierarchy details
 Run hierheight to set new level details
 Re-run AW enablement for BI Beans if using
CWM2 metadata
Add self calculating measures
(formulas)
 Use Analytic Workspace Manager to define
the formula
 Run add_cube utility to add to create a new
Standard Format (SF) cube
 Run set_measure_formula_properties utility to
add to the new cube
 Modify SQL views if using SQL queries
 Re-run AW enablement for BI Beans if using
CWM2 metadata
Viewing Data - Samples







BI Beans - Crosstab
Drill to Relational Detail (adhoc jtable)
Ad Hoc query tool
Beanie – Drill to Relational coming
Excel
Discoverer
SQL views – any query tool (olap_table)
BI Beans - Crosstab
Relational Table - jtable
Ad Hoc query tool
Beanie – Oracle Consulting
Excel
Summary
 Existing Star schema not required but
knowledge of data is
 Basic understanding of OWB necessary
 OLAP design understanding is critical
 Knowledge of OLAP structures and
functionality mandatory
 Can use Oracle Workflow to automate
updates
Next Steps….
 Interested in leveraging Oracle OLAP
–
Joseph Rayman – [email protected]
 443-253-2440
–
Carey Probst – [email protected]
 518-584-4388
–
Larry Anderson – [email protected]
 973-895-6113
QUESTIONS
ANSWERS
Reminder –
please complete the OracleWorld
online session survey
Thank you.