Oracle and Essbase Building a Data Warehouse
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Agenda
• Oracle’s Fusion
•
•
•
•
Essbase
OLAP, ROLAP, run laps
Dimensional Modeling
The Integration Console
• Metadata
• Creating a Cube
• Hierarchies
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• OLAP Metaoutline
• Dense and Sparse
• Creating the Essbase
Application
• The Administrative
Console
• OBIEE Integration
• What is OBIEE?
• An OBIEE Dashboard
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Oracle’s Fusion
• Oracle has acquired a number of companies
• A link between acquired technologies often creates
synergies
• This is especially true in the Business Intelligence (BI)
space
• Oracle’s Fusion is approach to middleware that creates
the links and synergies
• Example: Essbase now includes the Integration Console
– A full GUI development Environment
– Data from Relational Sources to OLAP
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Relational and OLAP
• The Relational Model is very powerful way to
abstract and solve business problems
– Objects in the world of business: invoices, orders and
so forth can be captured using relational tables
– The business object could be understood as a table,
and the business rules as the relationship
– Very powerful in supporting transactions and single
entries: Online Transaction Processing (OLTP)
– Reporting, however, had it’s problems
• Executives want to analyze summaries
• The enterprise view
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OLAP
• Online Analytical Processing
– New approach to thinking
• What are the ways we want to slice and dice
(dimensions)
• What the measures that are important to the
business (facts)
• Also can use the subject based approach to
analysis
• This talk is based on the dimensional modeling
approach
– Use transaction or operational data and create a
data warehouse
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Definition
According to Wikipedia
A data warehouse is a repository of an
organization's electronically stored
data.
Data warehouses are designed to
facilitate reporting and analysis [1]
1. Inmon, W.H. Tech Topic: What is a Data Warehouse? Prism Solutions.
Volume 1. 1995
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Two Leaders
Bill Inmon
Bill Inmon is universally recognized as the "father of the data warehouse." He has over 26 years
of database technology management experience and data warehouse design expertise, and
has published 36 books and more than 350 articles in major computer journals. His books have
been translated into nine languages. He is known globally for his seminars on developing data
warehouses and has been a keynote speaker for every major computing association. Before
founding Pine Cone Systems, Bill was a co-founder of Prism Solutions, Inc.
Ralph Kimball
Ralph Kimball was co-inventor of the Xerox Star workstation, the first commercial product to use
mice, icons, and windows. He was vice president of applications at Metaphor Computer
Systems, and founder and CEO of Red Brick Systems. He has a Ph.D. from Stanford in
electrical engineering, specializing in man-machine systems. Ralph is a leading proponent of the
dimensional approach to designing large data warehouses. He currently teaches data
warehousing design skills to IT groups, and helps selected clients with specific data warehouse
designs. Ralph is a columnist for Intelligent Enterprise magazine and has a relationship with
Sagent Technology, Inc., a data warehouse tool vendor. His book "The Data Warehouse Toolkit"
is widely recognized as the seminal work on the subject.
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Kimball’s Definition
A data warehouse is "a copy of
transaction data specifically
structured for query and analysis".
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Kimball’s Rules
• The data warehouse must make an organization’s
information easily accessible
• The data warehouse must present the organization’s
information consistently
• The data warehouse must be adaptive and resilient to
change
• The data warehouse must be a secure bastion that
protects our information
• The data warehouse must serve as a foundation for
improved decision making
• The business community must accept the data
warehouse if it is to be deemed successful
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Differences?
• Kimball and Inmon are very close in their definitions of a
data warehouse and the functions it provides. So how
do they differ?
– Inmon believes a data warehouse should be
based on a relational database using 3NF.
– Kimball designs a data warehouse with
intentional de-normalization with ease of use
for the business person as a key criteria
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Kimball’s Four Step Method
1. Identify the business process
2. Identify the grain
3. Choose dimensions
4. Identify numeric facts
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Identify the Business Process
• Natural business process
• Supported by a data gathering activity
• NOT a department or division – it is a process
–
–
–
Multiple departments may contribute to a process
Forces use of consistent vocabulary and labels
Departmental approach results in duplicate data
flows in a single process
• “Economical” Data Usage
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Identify the Grain
• This is the level of detail that will be associated
measurements in the model
• This is an extremely important step
• Will impact the selection of dimensions
• You may find in analysis for steps 3 and 4 that the
original selection was wrong. Don’t hesitate to go back to
step 2 and start over as soon as you find the problem.
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Identify the Grain
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Dimension Tables
• Dimensions are the “by” words in business intelligence
– Count Customers by Region
– Order Sales by Product
– Shipments by Quarter
• They are the entry point for business intelligence
• They are textual
– Words and terms business users understand
• They are discrete
• Often represent hierarchies
• Typically highly de-normalized
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Dimensions
• Nearly every BI application includes some type of time or date
dimension
– Include attributes in the date dimension for all units of time
measurements
– Don’t break time into separate measures, month dimension, day
dimension, etc.
• Always include a dimension total attribute
• Don’t be afraid to include all of the attributes of a dimension
– 50 attributes is not uncommon
– Think about the drill down
• Use surrogate keys for dimension
– Smart keys are never smart enough
– Natural Keys change
• Resist “Snowflaking”
• Be careful of too many dimensions, use dimension attributes in a denormalized fashion
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Fact Tables
• A row in a fact table corresponds to a measurement
– A measurement is a row in a fact table
– All measurements must have the same grain
• The most useful facts are numeric and additive
• Generally, we do not enter zeros when something has
not happened
• Three types are
– Transaction
– Periodic Snapshot
– Accumulating Snapshot
• Percentages and ratios are non-additive
– Store the numerator and denominator
– Remember to calculate the ratio of the sums, not the sum of the
ratio
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Star Schema
• The result is refereed to as a star schema, as the
dimensions arranged around the fact table resemble a
star!
From Wikipedia
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Cube
• Star Schema is also called a
Cube or a Multidimensional
Cube. The fundamental
structure for data in a
multidimensional (OLAP)
system.
• A cube contains dimensions,
hierarchies, levels, and
measures. Each individual
point in a cube is referred to
as a cell.
Source: http://www.sdgcomputing.com/glossary.htm
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Degenerate Dimensions
• Control numbers which are never added,
only used to count or group are candidates
for degenerate dimensions
• These are dimensions, but the descriptive
information has been pulled into other
dimensions
– Consider an order
• Customer
• Ship to
• Order date
– All pulled into other dimensions, only the
order number remains
• In fact table, identified with prefix dd_
• Degenerate Dimensions are often the grain
of the fact table
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What is OLAP
• An approach to business intelligence very
similar to dimensional modeling
• Facts and Dimensions are identified
• Then, multi dimensional cubes are built
– Each fact is associated with all appropriate
dimensions
– This where term multi-dimensional arises
• Data is gathered and aggregated from
operational systems
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OLAP Database Architectures
• There are several
options for the
physical structure
of an OLAP
database
• The key is
supporting data in
cubes for user
interactions
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Simplest OLAP example
May 2008
June 2008
July 2008
August 2008
Online Sales
23456234
5235
45
2345345
Offline Sales
2345234
2345234
3434
2345234
Introduce Hierarchies
May 2008
Online Sales
June 2008
July 2008
August 2008
23456234
5235
45
2345345
Internet
21110611
3403
18
2110811
Text Message
234623
1832
27
234535
2345234
2345234
3434
2345234
Catalog
2110711
1524402
1374
2110711
In Store
234523
820832
2060
234523
Offline Sales
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More Dimensions And Hierarchies
Product Group A
Apparel Type 1
SKU 3490857
Online Sales
SKU 2389057
Internet
Apparel Type 2
Text Message
SKU 934785
Offline Sales
SKU 90348
Catalog
SKU 348907
In Store
Year 1
Quarter 1
Month 1
Month 2
Month 3
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Logical View Fact Table
Sales Cube
Date Key
Product Key
Channel Key
Sales Fact 1
Sales Fact 2
• We add dimensions as appropriate
• Facts are sliced and diced as need
• Each entry is a cell
• Not every cell will have a fact – eg.
Sunday sales in a B2B environment
in cube with a daily grain
• The percentage of populated cubes
is called the sparsely or density of the
cube
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Query Language for an OLAP database
• Unlike relational databases, which had SQL
there is no equivalent in the OLAP world
• The first real standard API was OLE DB for
OLAP specification from Microsoft
– Appeared in 1997
– The MDX query language
– Adopted by most server and client OLAP vendors
• In 2001 Microsoft and Hyperion announced the
XML for Analysis specification,
• XML for Analysis also uses MDX
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The Exploding Database
• Exploding multidimensional databases are a
common
– Happens when a Multidimensional database gets so
large query performance degrades
– The amount of actual data is small, yet the database
is huge
• How does this happen?
– In part, because people are unfamiliar with multidimensional
databases, so the phenomenon is widely misunderstood
– multidimensional geometry can be counterintuitive
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Some Facts You Need to Understand
•
•
Multidimensional databases usually take data from other sources, such as
legacy systems, relational databases or desktop tools, such as
spreadsheets.
A few slides back we saw some of the common approaches to building a
multidimensional database
–
–
–
ROLAP -- the data is still physically stored in an RDBMS, usually in some form of star or
snowflake schema
MDB (multidimensional database), the data is physically stored in a different file structure,
optimized for multidimensional processing and fast retrieval.
Hybrid OLAP products, which allow both direct access to relational data for multidimensional
processing, as well as having their own optimized multidimensional disk storage for
aggregates and pre-calculated results.
Data is stored in an MDB will normally take much less space than it did in the
source system, even if it is not summarized
•
Multidimensional storage takes between a tenth and a half of the space taken to store
exactly the same information in a relational database.
–
–
This is mainly because the keys, indexes and dimensional structures are either not required
at all or take far less space.
Also, the sparsity is often better suppressed and the data may even be compressed
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Then, your database gets huge…
What happens next?
•
OLAP applications are intended for interactive use, people expect to get a
fast response to queries — ideally, not more than a few seconds
– Queries get slower and more complicated if a significant amount of
calculations have to be done
– hierarchical consolidations
– calculations of variances
– analyzing trends
– deriving computed measures
• In order to get a fast response, large multidimensional applications need to
pre-calculate some of the information for analysis.
This might include high level consolidations
• In MDBs, the storage of pre-calculated data is usually automatic and
transparent
• In ROLAPs, summary tables are normally used
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Queries die and never come back
The details of the implementation differ, there is no difference in
principle between MOLAP and ROLAP products in this.
•
•
•
•
•
Multidimensional cross relationships exist in all OLAP applications
The data is usually very sparse -- the vast majority of possible cells,
combinations of dimension members, contain no data.
Thinly distributed data values may have hundreds of computed dependent
cells
hierarchies in each dimension.
The ‘computed space’ is much denser than the original data
While it is hard to predict the exact value in advance, adding an
extra dimension to a multidimensional object with no increase
in the amount of input data will at least double the size of the
fully computed database.
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What to Do?
• Avoid fully pre-calculating any multidimensional object
with more than five sparse dimensions
–
–
–
–
Ratios
Variances
simple time series conversions
rarely viewed consolidations may all be computed on-the-fly
• Many products classify data using a concept variously called
attributes, properties or characteristics
• These are used for on-the-fly groupings, selections and
aggregations, without requiring the full overhead of a dimension
• Reduce the sparsity of individual data objects by good application
design
• Use a multicube rather than a hypercube approach
• so each object has the minimum number of necessary dimensions
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Essbase: An OLAP Product
• Essbase is a multidimensional database management
system (MDBMS)
– A platform to build analytic applications. Essbase,
– Its derives from "Extended Spread Sheet dataBASE“
• Originally developed by Arbor Software
• Arbor merged with Hyperion Software in 1998
• Hyperion Solutions Corporation was acquired by Oracle
Corporation in 2007
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Multidimensional
• Essbase is a general-purpose multidimensional database
• Developed to address the scalability issues associated with
spreadsheets
• Consider financial data in spreadsheet format:
Jan
Feb
Mar
Apr
Sales
232,234
342,342
1,234,345
34,345
Expenses
131,345
193618
691,233
20,607
Profit
100,889
148,724
543,112
13,738
Units
343
505
1,822
50
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Looks Like a Cube Already…
• Ask a question: How does it break down by region? Now we
have multiple spreadsheets
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How about a customer breakdown?
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Spreadsheet Nightmare, or…
• Apply our knowledge of dimensional modeling
– What is the business process?
• Sales
– What is the grain?
• Monthly
– What are the dimensions?
•
•
•
•
Time
Region
Customer
What else?!?!?
– What are the facts?
• Sales Amounts
• Expenses
• Units Sold
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Create a Hierarchy
•
Essbase includes an administrative tool which allows the analyst to define the
dimensions and their hierarchies
•
•
There are two "storage options“
Block Storage Option (Essbase BSO) or Essbase Analytics
–
–
–
–
•
Dense blocks allocate space for every potential cell in that block
Sparse dimensions only create blocks when required.
This implementation is hidden from front-end tools
Block storage effectively minimizes storage requirements without impacting
retrieval time, but it is limited by its treatment of aggregate data in large
applications
The second storage option is Aggregate Storage Option (Essbase ASO) or Enterprise
Analytics
– Essbase ASO does not store any aggregate values, but instead calculates
them on demand.
– Where runtime generation of these values is inconvenient, the database
materializes one or more aggregate "views“
– One aggregate level from each dimension
– This process can be partially automated
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And the Front End is familiar
• The front end tools for Essbase are familiar to users
– An Excel plug-in is the basic tool
– Users pull in data from a cube and Excel it as
rolled-up totals on dimension hierarchies
– Uses can then drill down or across to
investigate the cube
• SmartView is a new product that allows user to employ
any Microsoft Office product as their front end
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Dashboards and Essbase
• Oracle’s acquisition of Hyperion and Oracle’s Fusion
efforts means Essbase can be integrated with many of
Oracle’s other BI tools
– A star schema cube in an Oracle database
can be a data source for Essbase
– BI Publisher reports can connect to an
Essbase database
– An OBIEE dashboard can use an Essbase
cube as its data source
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Migration
• So, what about data stored in a Star Schema in a
relational data base
– How do we move it to an Essbase database?
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The Data Warehouse
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Configure and Use the Integration
Services Console
• Oracle has built a tool which
allows you to map objects in a
relational database to an
Essbase cube
• It is included as a component
in Essbase System – 9.
• Confirm that your Integration Server
is up and configured properly.
• Start the Integration Console, you
should see this dialogue
• Although there are drop down boxes,
you will have to type each of the
entries.
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Attach to the Star Schema
•
•
•
•
•
•
The Server is the name of the server on
which your Oracle database is running.
The Catalog ODBS DSN in this case is an
entry in the tnsname.ora file.
The prefix Oracle: identifies it as a
tnsnames entry to Essbase.
BSORCL is the identifier for this tnsnames
entry.
The Code Page is the language.
User Name here refers to the database
schema in which you would like an OLAP
Metadata catalog created.
– Here the catalog is being installed in
Scott/Tiger (bad practice)
•
Finally, press the Create button to create the
catalog and you are ready to log on.
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The next
dialogue
facilitates the
connection
between your
newly created
metadata
catalog and the
Essbase Server
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OLAP Model or an OLAP
Metaoutline?
Create the
OLAP model
first.
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Data Source?
• For simplicity in this
demo, the same
database that was
used to store the
metadata catalog is
also used to as the
data source.
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Build the OLAP Model
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Add Facts
Start by creating a
fact table.
You can select
Tools/Create
Fact Table from
the main menu
and select the
table to use
from the list of
tables.
Or simply highlight
the table name
you want to use
for your fact
table and drag it
to diagram.
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Time?
You will then be asked if you want to create a time dimension. Press No
– this demo will use the Transaction Date table for its time dimension.
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Accounts are Our Metrics
Finally, you will be asked if you want to create an Accounts dimensions.
Answer Yes, this is where the measures are stored in a multidimensional model.
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Link our Transaction Facts to
Accounts
Highlight the Transactions, right click and
use the tear-off menu to display the
columns
You can move and re-size
objects with standard GUI
functionality, grabbing
focus, sizing arrows, etc.
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Add Dimensions
Select Tools/Create Dimensions
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Next…
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Answer Yes and the Model is
Nearly Complete
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One table at a Time Offers More Control
Drag and
drop adds
tables to
the model
and lets
you
navigate
to the
dialogue
to create
links and
properties
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Now it is Time!
Finally, this model needs a time dimension. Transaction Date will work,
just double click on the table object in the diagram and a properties
dialogue will be displayed. Change the type to Time:
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Your Time Dimension will now show
a red title bar when selected.
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Give the Model a Name and…
Save Your Work!!
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Create Hierarchies
The meat of
Business
Intelligence are
the hierarchies
– the ability to
accumulate
and drill down
on dimensions
to spot trends,
correlations
and
relationships.
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Retailers and the Other Dimensions
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OLAP Metaoutline
• An Essbase installation is organized into applications.
• An application is a data set and a set of business rules
that forms a unit of interest to an analyst.
• There are several components, one of the chief
elements being a database to contain and organize the
cubes in the application.
• The next is a Metaouline that is used by the Essbase
application to organize and describe data and its
relationships.
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Point and Click…
• Begin by selecting
File/Open and then in the
New tab, selecting OLAP
Metaouline while the
model selection is the
OLAP Model just created.
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…And
Let the
Tool do
the
Work..
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Nice, eh?
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Drill Down
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Time to Think – are You Dense?
•
There are two types of storage available in Essbase applications
– Aggregate
– Block.
According to Edward Roske and Tracy McMullen compare the two storage options in
their book Look Smarter than You Are with Hyperion Essbase (interRel Press, 2007).
Block Storage Option (BSO) databases utilize…member formulas, blocks, dense and
sparse dimensions, and page and index files. Unfortunately, the block storage
architecture…starts to have performance issues as dimensionality and outline sizes
grow…
Aggregate storage option (ASO) databases were created to deal with … very large
sparse data sets with a high number and potentially millions of members. ASO
utilizes a new kind of storage mechanism that allows for improved calculation
times…the calculations just aren’t as complex.”
•
•
“Use BSO for applications that require complex calculations and write back
capabilities.
Use ASO for applications that require a large number of dimensions and members
that simply ‘roll up’ (i.e. minimal complex calculations are required).”
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This Cube is Dense
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Create an Essbase Application and Database
• Time has come to
use the Model and
Metaoutline you have
created to generate
an Essbase
application and
database.
• Select
Outline\Member
Load.
• A dialogue will
appear.
• Tell Essbase what to
name the new
Essbase application
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In the Calc Scripts section…
This insures that when the
new application is created,
the loader calculates
aggregates as well as adding
the detail level data to the new
application.
make sure
“Use default calc script”
is selected.
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Essbase creates a database with
approximately a million and half
records in about one minute
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Operations from Essbase’s Administration Console
• We will want to access the Essbase Outline
• The outline is the organization of the cube
• Clean up Essbase assigned names to make
them more user friendly
• Create Generations
• All done from the Essbase Administration
Console
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We can Review Data
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You can also view hierarchies in the outline
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Prepare for Integration with OBIEE
• Most of the work before you make the actual connection
between Essbase and OBIEE has to do with supplying
meaningful names and descriptive information for the
entries in your outline.
Start with the Retailers dimension.
A right click, select Generations
Enter the labels that are
to appear in the dashboard
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Working in OBIEE
• OBIEE stands for Oracle Business Intelligence
Enterprise edition
• Formerly known as Siebel Analytics
• When Oracle Corp. has acquired Siebel System
and decided to make Siebel Analytics their
flagship analytic engine and renamed it OBIEE
– A reporting component DASHBOARD
– Metadata Layer
– A repository
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Creating a New Repository
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Map it to Essbase
Essbase is a
Multidimensional
database
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The login is the Essbase owner.
Select Example1
Application
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Drag and Drop to Create…
The Physical layer
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Drag and Drop to Create…
The Physical layer
The Metadata layer
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Drag and Drop to Create…
The Physical layer
The Metadata layer
The Presentation layer
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Building a Dashboard
• The interface and reports are a system to users.
• Careful design of an interface is one of the critical
success factors in a BI project.
• OBIEE’s two main user facing components are
Dashboards and BI Publisher.
• Our valid repository is Example1.rpd1
• This step covers making that .rpd available to an OBIEE
dashboard
• Then, we will examine generation of a display.
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Step by Step
• Start by shutting down your BI servers
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by step by step
• Now create a folder for Example1 in your OBIEE catalog
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Configuration Settings
YOUR_ORACLEBI_HOME\OracleBIData\web\config\instanceconfig.xml
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No Worries
YOUR_ORACLEBI_HOME \OracleBI\server\Config\ NQSConfig.INI
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Restart the Servers, then…
We have Data!!!!
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Oracle Answers
Click on
column
names to
build an
Answer
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Check Your Answer
And Save
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Navigate to Dashboards
Select Edit Dashboard
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Add a page, a
section, and your
saved answer to
the new page.
Then Save.
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A dashboard with full functionality
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Conclusions
• Clearly, this dashboard is first step.
• The plumbing is in place, now you must complete the
rest of the house.
– Prompts,
– Meaningful displays
– pivot tables
• Small features add up to a big BI experience
• There are many other fine tutorials and online papers
that will help you refine your dashboard – now that you
have a multidimensional database supplying the cube
with the data.
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Thank You!
BravesoftTECH.
www.bravesoft.com
3131 South State Street
Suite 307
Ann Arbor MI 48108
877-734-2780
Fax: 734-786-8476
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