OBIEE Reporting Against Transactional Schemas
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Transcript of OBIEE Reporting Against Transactional Schemas
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Stewart Bryson, US Managing Director, Rittman MeadRittman Mead BI Forum, May 2012
Transactional Schemas with OBIEE 11g
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Who am I?
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Stewart Bryson, US Managing Director, Rittman Mead
Oracle ACEOracle BI/DW Architect and Delivery SpecialistCommunity Speaker and EnthusiastWriter for Rittman Mead Blog:
http://www.rittmanmead.com/blog
Developer of Transcend FrameworkEmail : [email protected] : @stewartbryson
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Who is Rittman Mead?
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Oracle BI&DW ExpertsConsulting, Training and SupportGlobal Company, Offices in the US, UK, India and
Australia
1 Oracle ACE Director, 2 Oracle ACEsLongest-running Oracle (BI) BlogVoted UKOUG BI Partner of the Year 2008, 2010, 2011Oracle Community Advocates
16 Sessions at Oracle Open World 2010 12 Sessions at Oracle Open World 2011
Twitter : @rittmanmead
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What Will We Talk About?
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Architectural decisionsDifferent types of transaction schemasApplication Express (Apex) Applications
Customer Tracking (CRM) Ask the Expert
See the RPD development processLearn some best practices along the way
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Why Do We Want to Do This?
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Data Warehouse is not an option Budgetary constraints Lack of conviction Failed BI projects in the past
Visionary Methodology Agile or iterative development Exadata
Paradigm Shifts Big Data Hadoop, MapReduce Endeca (?)
Because you can!
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Oracle Next-Generation DW Reference Architecture
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Oracle Next-Generation DW Reference Architecture
Staging Layer Change tables Reject tables External tables
Foundation Layer Typically third-normal form
(3NF) Process neutral Reporting-agnostic
Access and Performance Layer Dimensional model Star Schemas Process specific
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Oracle Next-Generation DW Reference Architecture
Recommend building a foundational layer instead of a replicated copy Insert-only copy of the
source No updates or deletes
Additional functionality Historical reporting Mimic slowly-changing
dimensionsFor simplicity sake, Im going
directly against the source for this presentation
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Oracle Business Intelligence Enterprise Edition (OBIEE) 11g
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Dashboards, Ad-hoc Reporting, Alerts, Microsoft Office Integration High quality graphical, role/user based views Multiple views of same data
Point and click ease of useCommon Enterprise Information Model
Unified semantic/logical view of data from multiple sources
Heterogeneous database access True enterprise deployment
Alerts, scheduling and distribution
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OBIEE Metadata and Data Layers
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OBIEE Metadata and Data Layers
Relational, OLAP, File and Application Data Sources
Physical Data Sources, suchas relational databases,OLAP Cubes and applicationsource adapters
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OBIEE Metadata and Data Layers
Relational, OLAP, File and Application Data Sources
A Semantic Model made upof Metrics, dimensions, hierarchies and calculations, derived from...
Physical Data Sources, suchas relational databases,OLAP Cubes and applicationsource adapters
Enterprise Semantic Model
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OBIEE Metadata and Data Layers
The Web Catalog containsdefinitions of reports, alertsdashboards, KPIs, based on...
Relational, OLAP, File and Application Data Sources
A Semantic Model made upof Metrics, dimensions, hierarchies and calculations, derived from...
Physical Data Sources, suchas relational databases,OLAP Cubes and applicationsource adapters
Web Catalog
Enterprise Semantic Model
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Flow of Data Through the Three-Layer Semantic Model
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Flow of Data Through the Three-Layer Semantic Model
Simplification of the Data ModelIntegration of Disparate Data Sources
Addition of Business Logic and CalculationsAddition of Aggregate Sources
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Two Types of Transactional Schemas
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Highly Normalized (3NF) Typical OLTP application Designed for high-scalability Hundreds or thousands of simultaneous
gets and puts Customer Tracking Application (CRM)
Two Types of Transactional Schemas
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Highly Normalized (3NF) Typical OLTP application Designed for high-scalability Hundreds or thousands of simultaneous
gets and puts Customer Tracking Application (CRM)
Highly Denormalized More denormalized than dimensional models Typically a single entity holding everything
about a transaction Weblogs, logging tables, etc. Our Ask the Expert Application
Two Types of Transactional Schemas
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Customer Tracking (CRM)
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Customer Tracking (CRM)
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Customer Tracking (CRM)
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Customer Tracking (CRM)
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Ask the Expert
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Ask the Expert
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Ask the Expert
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Ask the Expert
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Resolving Ambiguous (Multiple) Joins Between Tables
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A physical table may, by default, join to another physical table many times Multiple joins to a staff table for regional manager,
branch manager, etc. Multiple dates for order date, ship date, return date,
etc.
BI Server must have one, and only one, join route between each table
Therefore you must alias the joined table and join to each alias separately
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Resolving Ambiguous (Multiple) Joins Between Tables
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Resolving Ambiguous (Multiple) Joins Between Tables
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Resolving Ambiguous (Multiple) Joins Between Tables
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Resolving Ambiguous (Multiple) Joins Between Tables
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Resolving Ambiguous (Multiple) Joins Between Tables
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Extracting Simple Dimension Tables from 3NF
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Find an entity that we know we want to report on Customer makes a lot of sense for CRM Join our customer-specific entities in a Logical Table
Source (LTS)
Find a overall primary key for the logical dimension table Made up of multiple underlying tables Usually the key from the lowest level of granularity
Construct hierarchies that define the data Not much different than a dimensional model The logical dimension table now hides the
complexity
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Dimension Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Identify transaction tables as candidates Tables with important numeric values are easy Sometimes we have measure-less relationship
tables
Build the fact defining measures where needed Relationship tables might not have measures Kimball calls these factless fact tables Build a mock count measure
Transaction tables sometimes have textual attributes Build a logical dimension table for these Kimball calls these junk or mystery
dimensions
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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Extracting Simple Fact Tables from 3NF
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WITH SAWITH0 AS (select sum(1) as c1, T367.ACTIVITY_TYPE as c2, T396.CATEGORY as c3, T476.ID as c4, T476.CUSTOMER_NAME as c5, T500.STATUS as c6from CUST_TRACK.EBA_CUST_CATEGORIES T396, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_STATUS T500, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452, CUST_TRACK.EBA_CUST_ACTIVITY_TYPES T367where ( T367.ID = T452.ACTIVITY_TYPE_ID and T396.ID = T476.CATEGORY_ID and T452.CUSTOMER_ID = T476.ID and T476.STATUS_ID = T500.ID ) group by T367.ACTIVITY_TYPE, T396.CATEGORY, T476.CUSTOMER_NAME, T476.ID, T500.STATUS)select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5, D1.c6 as c6, D1.c1 as c7, 0 as c8, 0 as c9from SAWITH0 D1order by c3, c4, c5, c6, c2
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WITH SAWITH0 AS (select sum(1) as c1, T367.ACTIVITY_TYPE as c2, T396.CATEGORY as c3, T476.ID as c4, T476.CUSTOMER_NAME as c5, T500.STATUS as c6from CUST_TRACK.EBA_CUST_CATEGORIES T396, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_STATUS T500, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452, CUST_TRACK.EBA_CUST_ACTIVITY_TYPES T367where ( T367.ID = T452.ACTIVITY_TYPE_ID and T396.ID = T476.CATEGORY_ID and T452.CUSTOMER_ID = T476.ID and T476.STATUS_ID = T500.ID ) group by T367.ACTIVITY_TYPE, T396.CATEGORY, T476.CUSTOMER_NAME, T476.ID, T500.STATUS)select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5, D1.c6 as c6, D1.c1 as c7, 0 as c8, 0 as c9from SAWITH0 D1order by c3, c4, c5, c6, c2
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Extracting Simple Facts and Dimensions
Does This Make Sense?
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Extracting Complex Dimension Tables from 3NF
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Dimensional models combine multiple entities in a single dimension Completely contained in a single hierarchy More than just removing snowflakes
Dimensional models use a Date DimensionMultiple hierarchies
Users want the data to rollup in different ways There is always more than one route from Grand
Total to Detail
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Transactional versus Dimensional
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Transactional versus Dimensional
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Transactional versus Dimensional
Logical Fact
Logical Dimension
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Transactional versus Dimensional
Logical Fact
Logical Dimension
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Transactional versus Dimensional
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Transactional versus Dimensional
Think more transactionally Is similarity to the OLTP system more
intuitive? Is it easier to model? Will we get better performance?
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Transactional versus Dimensional
Think more transactionally Is similarity to the OLTP system more
intuitive? Is it easier to model? Will we get better performance?
Think more dimensionally This is the way that business users think
about their data Modeling effort is about the same The BI Servers ability to prune unneeded
tables from an LTS makes performance negligible
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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Combining Multiple Entities in a Single Dimension
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WITH SAWITH0 AS (select sum(1) as c1, T476.CUSTOMER_NAME as c2from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452where ( T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) group by T476.CUSTOMER_NAME)select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3 from ( select distinct 0 as c1, D1.c2 as c2, D1.c1 as c3from SAWITH0 D1order by c2 ) D1 where rownum
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WITH SAWITH0 AS (select sum(1) as c1, T476.CUSTOMER_NAME as c2from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452where ( T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) group by T476.CUSTOMER_NAME)select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3 from ( select distinct 0 as c1, D1.c2 as c2, D1.c1 as c3from SAWITH0 D1order by c2 ) D1 where rownum
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Building a Date Dimension
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Transactional systems do not have date dimensions Single record gets and puts Date functions are sufficient
Dimensional models use date dimensions We dont have one in our transactional schema We need one to build our date hierarchies OBIEE requires one for time-series functionality
Two options Build a date dimension in your reporting
environment joining dates to dates Construct a logical date dimension with what you
have
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Transactional systems do not have date dimensions Single record gets and puts Date functions are sufficient
Dimensional models use date dimensions We dont have one in our transactional schema We need one to build our date hierarchies OBIEE requires one for time-series functionality
Two options Build a date dimension in your reporting
environment joining dates to dates Construct a logical date dimension with what you
have
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Building a Date Dimension
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Building a Date Dimension
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Building a Date Dimension
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Building a Date Dimension
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Building a Date Dimension
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Building a Date Dimension
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Building a Date Dimension
Monday, 21 May 12
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Building a Date Dimension
Monday, 21 May 12
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Building a Date Dimension
Monday, 21 May 12
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Building a Date Dimension
Monday, 21 May 12
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Building a Date Dimension
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WITH SAWITH0 AS (select sum(1) as c1, RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')) as c2, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999') as c3, T476.CUSTOMER_NAME as c4from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452where ( T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) group by T476.CUSTOMER_NAME, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999'), RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')))select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c1 as c5, 0 as c6from SAWITH0 D1order by c4, c3, c2
Monday, 21 May 12
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WITH SAWITH0 AS (select sum(1) as c1, RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')) as c2, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999') as c3, T476.CUSTOMER_NAME as c4from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452where ( T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) group by T476.CUSTOMER_NAME, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999'), RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')))select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c1 as c5, 0 as c6from SAWITH0 D1order by c4, c3, c2
Monday, 21 May 12
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WITH SAWITH0 AS (select sum(1) as c1, RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')) as c2, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999') as c3, T476.CUSTOMER_NAME as c4from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452where ( T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) group by T476.CUSTOMER_NAME, TO_NUMBER(TO_CHAR(T452.ACTIVITY_DATE, 'yyyy'), '9999'), RTRIM(TO_CHAR(T452.ACTIVITY_DATE, 'MON')))select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c1 as c5, 0 as c6from SAWITH0 D1order by c4, c3, c2
Monday, 21 May 12
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Extracting Complex Fact Tables from 3NF
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Facts built from multiple sources are common in DW environments Sales table built from a CRM plus an e-commerce
application Activity fact tables that combine multiple types of
activity
This is fairly simple with ETL processing Multiple mappings loading the same fact table A single mapping doing complex union logic
Do we have the same capability available to us in the OBIEE Business Model?
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Complex Fact Tables: Multiple Activity Sources
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
Monday, 21 May 12
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Complex Fact Tables: Multiple Activity Sources
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WITH SAWITH0 AS (select 1 as c1, T476.CUSTOMER_NAME as c2, T367.ACTIVITY_TYPE as c3, T452.ACTIVITY_DATE as c4from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452, CUST_TRACK.EBA_CUST_ACTIVITY_TYPES T367where ( T367.ID = T452.ACTIVITY_TYPE_ID and T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) ),SAWITH1 AS (select 1 as c1, T476.CUSTOMER_NAME as c2, T476.CREATED_ON as c3from CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_ASSOCIATIONS T386where ( T386.CUSTOMER_ID = T476.ID ) ),SAWITH2 AS ((select TO_NUMBER(TO_CHAR( TRUNC(D0.c4), 'yyyy'), '9999') as c2, D0.c2 as c3, 'Explicit' as c4, D0.c3 as c5, D0.c1 as c6from SAWITH0 D0union allselect TO_NUMBER(TO_CHAR( TRUNC(D0.c3), 'yyyy'), '9999') as c2, D0.c2 as c3, 'Implicit' as c4, 'Customer Account Creation' as c5, D0.c1 as c6from SAWITH1 D0)),SAWITH3 AS (select sum(D3.c6) as c1, D3.c2 as c2, D3.c3 as c3, D3.c4 as c4, D3.c5 as c5from
(continued...)
Monday, 21 May 12
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WITH SAWITH0 AS (select 1 as c1, T476.CUSTOMER_NAME as c2, T367.ACTIVITY_TYPE as c3, T452.ACTIVITY_DATE as c4from CUST_TRACK.EBA_CUST_ASSOCIATIONS T386, CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_CUST_ACTIVITY T452, CUST_TRACK.EBA_CUST_ACTIVITY_TYPES T367where ( T367.ID = T452.ACTIVITY_TYPE_ID and T386.CUSTOMER_ID = T476.ID and T452.CUSTOMER_ID = T476.ID ) ),SAWITH1 AS (select 1 as c1, T476.CUSTOMER_NAME as c2, T476.CREATED_ON as c3from CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_ASSOCIATIONS T386where ( T386.CUSTOMER_ID = T476.ID ) ),SAWITH2 AS ((select TO_NUMBER(TO_CHAR( TRUNC(D0.c4), 'yyyy'), '9999') as c2, D0.c2 as c3, 'Explicit' as c4, D0.c3 as c5, D0.c1 as c6from SAWITH0 D0union allselect TO_NUMBER(TO_CHAR( TRUNC(D0.c3), 'yyyy'), '9999') as c2, D0.c2 as c3, 'Implicit' as c4, 'Customer Account Creation' as c5, D0.c1 as c6from SAWITH1 D0)),SAWITH3 AS (select sum(D3.c6) as c1, D3.c2 as c2, D3.c3 as c3, D3.c4 as c4, D3.c5 as c5from
(continued...)
Monday, 21 May 12
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WITH SAWITH0 AS (select sum(1) as c1, TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999') as c2, T476.CUSTOMER_NAME as c3, T476.ID as c4, 'Implicit' as c5, 'Customer Account Creation' as c6from CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_ASSOCIATIONS T386where ( T386.CUSTOMER_ID = T476.ID and TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999') = 2012 ) group by T476.CUSTOMER_NAME, T476.ID, TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999'))select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5, D1.c6 as c6, D1.c1 as c7, 0 as c8, 0 as c9from SAWITH0 D1order by c3, c4, c6, c2, c5
Monday, 21 May 12
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WITH SAWITH0 AS (select sum(1) as c1, TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999') as c2, T476.CUSTOMER_NAME as c3, T476.ID as c4, 'Implicit' as c5, 'Customer Account Creation' as c6from CUST_TRACK.EBA_CUST_CUSTOMERS T476, CUST_TRACK.EBA_CUST_ASSOCIATIONS T386where ( T386.CUSTOMER_ID = T476.ID and TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999') = 2012 ) group by T476.CUSTOMER_NAME, T476.ID, TO_NUMBER(TO_CHAR( TRUNC(T476.CREATED_ON), 'yyyy'), '9999'))select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5, D1.c6 as c6, D1.c1 as c7, 0 as c8, 0 as c9from SAWITH0 D1order by c3, c4, c6, c2, c5
Where did the union go?
Monday, 21 May 12
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Extracting Complex Facts and Dimensions
Does This Make Sense?
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Modeling Highly-Denormalized Schemas
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Some applications are almost model-less Files Weblogs Thin applications
OBIEE doesnt accept a single logical table in the Business Model Need at least one logical fact table Need at least one logical dimension table Usually well have multiple logical dimensions
How would we do this in the OBIEE Business Model? Use multiple aliases to a single table for facts and
dimension LTSs? Use the same physical table directly for each LTS?
Monday, 21 May 12
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Some applications are almost model-less Files Weblogs Thin applications
OBIEE doesnt accept a single logical table in the Business Model Need at least one logical fact table Need at least one logical dimension table Usually well have multiple logical dimensions
How would we do this in the OBIEE Business Model? Use multiple aliases to a single table for facts and
dimension LTSs? Use the same physical table directly for each LTS?
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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WITH SAWITH0 AS (select sum(1) as c1, T140.CATEGORY as c2, T140.SUBJECT as c3, T140.ID as c4from ASKTOM.ATE_SUBMITTED_QUESTIONS T140group by T140.CATEGORY, T140.ID, T140.SUBJECT),SAWITH1 AS (select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c1 as c4, D1.c4 as c6from SAWITH0 D1),SAWITH2 AS (select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, sum(D1.c4) as c5from SAWITH1 D1group by D1.c1, D1.c2, D1.c3, D1.c4)select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5 from ( select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, sum(D1.c5) over (partition by D1.c2) as c5from SAWITH2 D1order by c1, c2 ) D1 where rownum
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WITH SAWITH0 AS (select sum(1) as c1, T140.CATEGORY as c2, T140.SUBJECT as c3, T140.ID as c4from ASKTOM.ATE_SUBMITTED_QUESTIONS T140group by T140.CATEGORY, T140.ID, T140.SUBJECT),SAWITH1 AS (select distinct 0 as c1, D1.c2 as c2, D1.c3 as c3, D1.c1 as c4, D1.c4 as c6from SAWITH0 D1),SAWITH2 AS (select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, sum(D1.c4) as c5from SAWITH1 D1group by D1.c1, D1.c2, D1.c3, D1.c4)select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, D1.c5 as c5 from ( select D1.c1 as c1, D1.c2 as c2, D1.c3 as c3, D1.c4 as c4, sum(D1.c5) over (partition by D1.c2) as c5from SAWITH2 D1order by c1, c2 ) D1 where rownum
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Common to add another logical fact table with some of the same dimensional attributes
We have the Question information, but now we want to add the Review information
Build conformed logical dimensions out of disparate single attributes in two tables Submitter/Reviewer Submission Date/Review Date Submission/Review information
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Monday, 21 May 12
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T : (888) 631 1410 or +44 (0) 8446 697 995 E : [email protected] W: www.rittmanmead.com
Modeling Highly-Denormalized Schemas
Does This Make Sense?
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Summary
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Myth: OBIEE is only for reporting against Star Schemas Siebel first acquired nQuire Server to do
effective reporting against Siebel CRM system.
The Key to effective OLTP reporting is creative use of Logical Table Sources (LTS)
OBIEE does table pruning only using what it needs to in the LTS
We dont have to use separate aliases when a table acts as both a fact and a dimension
Monday, 21 May 12
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Stewart Bryson, US Managing Director, Rittman MeadRittman Mead BI Forum, May 2012
Transactional Schemas with OBIEE 11g
Monday, 21 May 12