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7/28/2019 Essbase Introduction
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Essbase Introduction
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2
Hyperion Product Suite
Hyperion
Hyperion BI+
Reporting
Hyperion BI+
ApplicationHyperion BI+ Data
Management
HFM (Hyperion
Financial Management)
HSF (Hyperion
Strategic Financial)
Hyperion
Planning
HPM (Hyperion
Performance
Management)
MDM (Maser
Data
Management)
FDQM (Financial
Query Data
Management)
HAL (Hyperion
Application Link)
DIM (Data
Integrated
Management)
Hyperion
Essbase
Analyzer
Reports
Interacting
Reports
Production
Reporting
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What is Essbase?
It is a multidimensional database that enables Business Users to
analyze Business data in multiple views/prospective and atdifferent consolidation levels. It stores the data in a multi
dimensional array.
Minute->Day->Week->Month->Qtr->Year
Product Line->Product Family->Product Cat->Product sub Cat
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Typical Data Warehouse Architecture
Operational
Systems/Data
Select
Extract
Transform
Integrate
Maintain
Data
Preparation
Data
Marts
Data
Warehouse
(OLAP
Server or
RDBMSData
Repository)
Metadata
ODS
Metadata
Select
Extract
Transform
Load
Data
Preparation
Multi
-
tiered Data Warehouse with ODS
Data StageData Stage
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Life Cycle Of Essbase
1.Creating the Database2.Dimensional Building
3.Data Loading
4.Performing the Calculations5.Generating the Reports
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Essbase Multi Dimension Data Modeling (Complete Life Cycle)
Physical Data Model Physical Tables from ODS Environment
Logical Multi Dimensional Model
Multi Dimensional View
Presentation Layer Reporting
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HYPERION Essbase Components1) Essbase Analytic Server (Essbase Server)
2) Essbase Administration Server (User Interface)
3) Essbase Integration Services (RDBMSEssbase)
4) Essbase Spread Sheet Services5) Essbase Provider Services.
6) Essbase Smart-view
7) Essbase Studio (New Feature)
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1.Client tier
2.Middle Tier (App tier)
3.Database tier
Essbase Architecture
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Architecture
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Contents
Overview (OLAP)
Multidimensional Analysis
* Multidimensional Analysis Introduction
* Operations In multidimensional Analysis
* Multidimensional Data Model
* Multi-Dimensional vs. Relational
Overview of system 9.x/11.x
* Hyperion System 9 Smart view
* Hyperion System 9 BI+ Interactive reporting
* Hyperion System 9 BI+ Analytic services
* Hyperion system 9 shared services
* Hyperion system 9 White Board
Introduction to Essbase
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Multidimensional Viewing and Analysis
Sales Slice of the Database
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Online Analysis Processing(OLAP)
It enables analysts, managers and executives to gain insight into datathrough fast, consistent, interactive access to a wide variety of possibleviews of information that has been transformed from raw data to reflectthe real dimensionality of the enterprise as understood by the user.
Data
Warehouse
Time
Product
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Overview of OLAP
OLAP can be defined as a technology which allows the users to view the aggregate
data across measurements (like Maturity Amount, Interest Rate etc.) along with a set ofrelated parameters called dimensions (like Product, Organization, Customer, etc.)
Relational OLAP (ROLAP)
Relational and Specialized Relational DBMS to store and
manage warehouse data
OLAP middleware to support missing pieces Optimize for each DBMS backend
Aggregation Navigation Logic
Additional tools and services
Example: Micro strategy, MetaCube (Informix)
Multidimensional OLAP (MOLAP) Array-based storage structures
Direct access to array data structures
Example: Essbase (Arbor), Accumate (Kenan)
Domain-specific enrichment
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OLAP
HOLAPMOLAPROLAP
Relational
OLAPMultidimensional
OLAP
Hybrid
OLAP
Implementation Techniques
MOLAP - Multidimensional
OLAP
Multidimensional
Databases for database
ROLAP - Relational
OLAP
Access Data stored
in relational Data
Warehouse for
OLAP Analysis
HOLAP - Hybrid OLAP
OLAP Server routes
queries first to MDDB,
then to RDBMS and result
processed on-the-fly in
Server
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Key Features of OLAP applications
Multidimensional views of data
Calculation-intensive capabilities
Time intelligence
**Key to OLAP systems are multidimensional databases.
Multidimensional databases not only consolidate and calculate data; theyalso provide retrieval and calculation of a variety of data subsets.
A multidimensional database supports multiple views of data sets for userswho need to analyze the relationships between data categories
Ex: Did this product sell better in particular regions? Are there regionaltrends?
Did customers return Product A last year? Were the returns due to productdefects?
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What is Multidimensional Analysis
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A multidimensional database supports multiple views of data sets for users who need
to analyze the relationships between data categories. For example, a marketing
analyst might want answers to the following questions:
How did Product A sell last month? How does this figure compare to
sales in the same month over the last five years? How did the productsell by branch, region, and territory?
Did this product sell better in particular regions? Are there regional trends?
Multidimensional databases consolidate and calculate data to provide
different views. Only the database outline, the structure that defines all elements ofthe database, limits the number of views. With a multidimensional database, users can
pivotthe data to see information from a different viewpoint, drill down to find more
detailed information, or drill up to see an overview.
Multidimensional Analysis
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Multidimensional Analysis
Analysis of data from multipleperspectives.
Sales Report By Month
All Products Customer Product
Month Jan Feb Mar
Gross Sales 2,358,610 2,345,890 58,860
Discount 116,616 138,856 20,567
Net Sales 2,477,428 2,566,526 89,196
Jan Gross Sales For all the products and all
customers in the current year. This will give the
details that which customer bought the most sales
and which product sold least in a month and year
Product Report By Month
Gross Sales Customer Product
Month Jan Feb Mar
Performance 1,597,560 1,697,890 775,600
Values 116,616 138,856 20,567
All Products 2,358,610 2,566,526 89,196
Variance Report By Channel
All Products Gross Sales Jan
Gross Sales Current Year Budget Act Vs Bud
Performance 775,600 1,697,890 224,160
Values 116,616 1,651,006 20,567
All Products 2,358,610 2,566,526 89,196
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OLAP Operations
Drill Down
Time
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
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OLAP Operations
Drill Up
Time
Product
Category e.g Electrical Appliance
Sub Category e.g Kitchen
Product e.g Toaster
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OLAP Operations
Slice and Dice
Time
ProductProduct=Toaster
Time
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OLAP Operations
Pivot
Time
Product
Region
Product
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Operations In multidimensional Analysis
Aggregation (roll-up)
dimension reduction: e.g., total sales by city
summarization over aggregate hierarchy: e.g., total sales by city and year -> total sales by region and by year
Selection (slice) defines a sub cube
e.g., sales where city = Palo Alto and date = 1/15/96
Navigation to detailed data (drill-down)
e.g., (sales - expense) by city, top 3% of cities by average income
Visualization Operations (e.g., Pivot)
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Database is a set offacts(points) in a multidimensional space
A fact has a measuredimension
quantity that is analyzed, e.g., sale, budget, Operating Exp,
A set ofdimensions on which data is analyzed
e.g. , store, product, date associated with a sale amount
Dimensions form a sparsely populated coordinate system
Each dimension has a set ofattributes
e.g., owner city and county of store
Attributes of a dimension may be related by partial orderHierarchy: e.g., street > county >city
Lattice: e.g., date> month>year, date>week>year
Multidimensional Data Model
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Uses a cubemetaphor to describe data storage.
An Essbase database is considered a cube, with
each cube axis representing a different dimension,
or slice of the data (accounts, time, products, etc.)
All possible data intersections are available to theuser at a click of the mouse.
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Multidimensional Data
10
47
30
12
Juice
Cola
Milk
Cream
Sales Volume
as a function
of time, city
and product
3/1 3/2 3/3 3/4
Date
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A Visual Operation: Pivot (Rotate)
10
47
30
12
Juice
Cola
Milk
Cream
3/1 3/2 3/3 3/4
Date
Product
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Multidimensional Viewing and Analysis
Consider the three dimensions in a databases as Accounts, Time, and
Scenario where Accounts has 4 members, Time has 4 members and
Scenario has two members.
Three-Dimensional Database
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The shaded cells is called a slice illustrate that, when you refer to Sales,you are referring to the portion of the database containing eight Sales
values.
Multidimensional Viewing and Analysis
Sales Slice of the Database
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Multidimensional Viewing and Analysis
Actual, Sales Slice of the Database
When you refer to Actual Sales, you are referring to the four Sales values
where Actual and Sales intersect as shown by the shaded area.
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Multidimensional Viewing and Analysis
Data value is stored in a single cell in the database. To refer to a specific data
value in a multidimensional database, you specify its member on each
dimension. The cell containing the data value for Sales, Jan, Actual is shaded.
The data value can also be expressed using the cross-dimensional operator (-
>) as Sales -> Actual -> Jan.
Sales -> Jan -> Actual Slice of the Database
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Multidimensional Viewing and Analysis
Data for January
Data for February
Data for Profit Margin
Data from Different Perspective
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Multi-dimensional database are usually queried top-downthe user starts at the top and drills intodimensions of interest.
- Can perform poorly for transactional queries
Relational databases are usually queried bottom-upthe user selects the desired low level data andaggregates.
- Harder to visualize data; can perform poorly forhigh-level queries
Multi-Dimensional vs. Relational
Total Products
P01 P02 P03
P01 P02 P03
Total Products
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OLAP Vs RDBMS
In RDBMS, we have:
DB -> Table -> Columns -> Rows
In OLAP, we have:
CUBES
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