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Unlocking the Secrets to
 How Essbase Thinks

           Edward Roske
        eroske@interrel.com
   BLOG: LookSmarter.blogspot.com
     WEBSITE: www.interrel.com
         TWITTER: ERoske
About interRel
• 2008 & 2009 Oracle Titan Award winner - EPM Solution of the
  year
• 2008 Oracle EPM Excellence Award
• 2009 Oracle EPM/BI Innovation Award
• One of the fastest growing companies in the world
  (Inc. Magazine, ‘08 & ‗09)
• Two of the four Hyperion Oracle ACE Directors in the world
• Oracle Gold Specialized Partner in Oracle EPM
• Focused exclusively on Oracle Hyperion EPM software
   – Consulting
   – Training
   – Infrastructure and Installation
   – Support
   – Software sales
• 5 Hyperion Books Available:
                                                            –   Essbase (7): Complete Guide
                                                            –   Essbase System 9: Complete Guide
                                                            –   Essbase System 9: End User Guide
                                                            –   Smart View 11: End User Guide
                                                            –   Essbase 11: Admin Guide
                                                            –   Hyperion Planning for End Users


                                                       • Coming in March
                                                            – Hyperion Planning for Administrators

                                                       •   To order, check out www.LuLu.com




3   Copyright © 2007, Hyperion. All rights reserved.
Agenda

•   Introduction
•   Internal Workings of Essbase: BSO
•   Internal Workings of Essbase: ASO
•   Question and Answer
Internal Workings of Essbase:
         Block Storage
Bob Earle Invented ―Sparse and Dense‖

•   How is data distributed?




                       SPARSE                          DENSE
                        Products                      Time Periods
                   X                              X X        X



                                       Accounts
                              X                   X X      X X       X
       Locations




                   X                              X    X   X         X
                       X                               X   X   X     X
                                   X              X        X   X     X
Block Structure



                       Var%
                      Var
                    Bud
                  Act
          UnitsSold
           Headcnt
              Profit
Account
(dense)




           Expense
          OtherExp
          Marketing
             Salary
               Rev
             COGs
              Sales
                       Jan   Feb   Mar   Q1   Q2   Q3   Yr
                                     Period
                                    (dense)
Data Cells Within the Block




                        Actual -> Profit -> Jan




Cross-dimensional operator (means Actual BY Profit BY Jan)
Blocks

   •   An individual block is created for each combination of sparse
       stored members


                               Cola->East




Cola->New York           Cola->Florida       Cola->Massachusetts
Storage and Compression
Storage

•   PAG File
     –   Contains the data (the blocks with a header for each)
     –   Contains up to 2 Gb of data in each PAG file (32-bit)
     –   Can be 1,024 different files
     –   Can be compressed and fragmented
     –   Can be stored on multiple drive locations


•   IND File
     –   Contains the list or pointers to the blocks (intersection of sparse dimensions)
     –   Contains up to 2 Gb of index in each IND file (32-bit)
     –   Can be 1,024 different files
     –   Can be fragmented
     –   Can be stored on multiple drive locations
Compression
•   ―Simple‖ compression settings
     – None
     – zLib
     – Index Value Pair
         • Can‘t assign directly
         • Good for really large blocks with really sparse data
•   Following types use multiple compressions (one per block)
     – Bitmap
         • Good for non-repeating data
         • Will use Bitmap or IVP
     – RLE = Run Length Encoding
         • Good for data with zeros and data that repeats (such as
           budgeting)
         • Will use RLE, Bitmap, or IVP
Dimension Order & Compression

•   Dimension order affects compression
•   First dense dimension determines your ―columns‖ in PAG file
•   Compression is from left to right, top to bottom
•   Move Time to first dense dimension and we get:

     BUDGET        Jan      Feb      Mar       Apr      May       Jun
      Sales         100       100      100      120       120     120
      COGS           50        50       50       50         50     50
     Margin          50        50       50       70         70     70
     Exp.            30        30       30       30         30     30
    Profit           20        20       20       40         40     40
• Notice repeating values
• Time should be dense then Measures for RLE compression
Calculations
Calculation Process




Accounts                 Jan      Feb     Mar   Qtr1
Sales                    124.71 119.43 161.93 406.07
COGS                      42.37   38.77  47.28 128.42
  Margin                 82.34    80.66 114.65 277.65
Dense Calculation




                                       After calc of Time
                        Year
                                           dimension
                        Qtr1
 Data load from table

XXXX XXXXXXX Jan
                                                  After calc of
 XX    ###
                                                   Accounts
 XX    ###   Feb                                   dimension
 XX    ###   Mar

                               Sales    COGS Margin Profit
Sparse Calculation


                  East -> Cola                       Calculated
                                                      blocks
                                                     Upper-level
                                                      blocks

                                                     Input blocks
                                                     Level zero
                                                      blocks
Vermont -> Cola                  New York -> Cola
Calculation Order: All Dimensions

•   First, Accounts
•   Second, Time
•   Third, remaining dense dimensions
•   Fourth, remaining sparse dimensions
•   Two Pass Calculation
Calculations

•   Default Calc - simplest method
•   Calc scripts
•   Dynamic calculations
•   Intelligent calculation
Commit Blocks

•   Using Uncommitted Access
     – When Commit Level is reached, blocks write to hard drive


•   Default is 3000 blocks

•   Setting Commit Blocks to Zero
     – Writes at completion of the entire transaction
     – Will dramatically improve calculation time
     – Will fragment your PAG file during a calculation
Retrievals
Index Cache


Index pages   New requests   Index pages on
     in                       physical disk
index cache




              Old requests




 Memory                           Disk
Data Cache


                 New requests   Disk blocks on
Disk blocks in                   physical disk
 data cache




                 Old requests




  Memory                            Disk
Internal Workings of Essbase:
      Aggregate Storage
BSO Limitations

•   Financial applications are more densely populated so BSO works great in
    those instances
•   BSO engine can handle sparse data but on a ―limited‖ scale
•   Outline size limited
•   Batch times required for loads and calcs
Aggregate Storage Option

•   Remember all the concepts we just learned:
     –   Dense / Sparse
     –   Index / page files




          X
     –   Cache settings
     –   Block storage
Aggregate Storage Databases

•   Similar to a BSO database – outline, dimensions, hierarchies… BUT
•   Different method for storing/calculating databases
•   New storage kernel built to handle ASO databases
•   Calculates 10-100x faster
•   Stores up to 252 dimension combinations
Aggregate Storage Databases
•   ASO addresses the following types of databases:
     – Read-only*
         • Write back to level zero available in 9.3.1
     – ―Rack and stack‖
     – Large dimensions
•   New Types of databases are possible
     – Customer analysis—data is analyzed from any dimension and there are potentially
       millions of customers
     – Procurement analysis—many products are tracked across many vendors
     – Logistics analysis—near real-time updates of product shipments
     – Market Basket analysis—products purchased along with other products
When to use ASO

•   Database is sparse and has many dimensions, and/or the dimensions have
    many levels of members
•   Database is used primarily for read-only purposes, with few or no data
    updates
•   Calculation of the database is frequent, is based mainly on summarizing of the
    data, and does not rely on calculation scripts

•   Starting in Essbase 11x, ASO should be your default/starting idea
BSO vs. ASO


BSO Outline          ASO Outline
End User Perspective

•   End users won‘t care whether their database is ASO or BSO
•   The way users access ASO is the same as BSO
     – Excel Add-in
     – Financial Reporting, Web Analysis, etc.
     – Smart View Add-in
•   Repeat… no differences (just more data/dimensions)
ASO Benefits from IT Perspective

•   Faster load and calc times provide
     – Lower hardware costs
     – Lower maintenance costs
     – Higher availability
•   Small disk footprints
•   Efficient tuning for storage and query response
How does ASO work?

•   Simple question with not so simple answer

•   Asked greatest minds in the business how ASO works and got the same
    resounding answer:

•   ―It‘s a black box‖ or ―it's top secret and hard to understand‖

•   There must be a better answer!
ASO is Designed For…

•   More dimensions and members
•   Less time required for batches
     – Fast aggregation of sparse data sets
     – Faster loads
     – Incremental loads
•   Reduction in database footprint
Key Concepts

•   Storage
•   Sparse data
•   Indexing
•   Aggregation
•   Nodes
ASO Storage (ROLAP in disguise)

•   ASO can also be said to be ―ROLAP with a super fancy index
    scheme that rules.‖

•   Big difference between ASO and BSO and ROLAP is the ASO
    storage mechanism

•   ROLAP stores data in a table and indexes a combination key
    between the rows

•   Essbase stores the concept of a cube of data in multiple
    dimensions or rather multiple keys
ASO Storage (ROLAP in disguise)

•   It‘s a multidimensional index

•   ASO takes it a step further and indexes the indexes in a way for more rapid
    aggregation of data

•   Storage is no longer in "blocks" but in highly optimized aggregation nodes

•   Visualize it as an asymmetric fractal Christmas tree flattened out and then
    indexed again
How does ASO work


•   Load Data only at level 0




•    Create aggregate views
•    Algorithm selects and stores ―most taxing‖ queries
•    Dynamic queries at runtime and increased
    speed by leveraging nearest stored view
ASO Concepts

• Concept of stored and dynamic hierarchies
    – Stored hierarchies can only aggregate
    – Dynamic hierarchies can utilize formulas and advanced unary operators
• Formulas on dimension members use MDX syntax
• Pre-aggregated views can be defined to help query performance
• Aggregated design wizard helps you create aggregation scripts
• Outline paging helps you page portions of the outline in and out
  of memory to assist in performance
• You can convert BSO outlines to ASO outlines using a wizard
Storage and Compression
Directory Structure

•   Directory structure differs in both content and purpose from BSO
•   Tablespaces are utilized to store data and metadata
     –   Default – stores numeric data (.dat file)
     –   Log – records database activity
     –   Metadata – stores metadata information about the objects in the database
     –   Temp – temporary working space for the Essbase kernel
Tablespace Overview

•   Defines the database storage in the form of file locations
•   Each ASO application has 4 tablespaces
     –   Default – database values
     –   Temp – temporary work space
     –   Log – transaction log files
     –   Metadata – database data structure
•   File location specifies a physical disk space for storing database
    files
•   Each tablespace may contain one or more file locations
     – Can span multiple physical drives and/or logical volumes
Manage Tablespaces
Sizing Tablespaces

•   During the data load and aggregation process, data is stored in both the
    Temp and Default directories
•   ASO will always build the full .dat file in the temp tablespace while the default
    tablespace still has the production .dat
•   Hence, for your maximum drive size you have to plan on AT LEAST 3x your
    max bloated .dat size if you want to be "safe" (buffer to temp to default)
Compression Dimension ASO
• In old releases, the Accounts dimension enabled database
  compression
• Beginning in 9.3, the Accounts dimension is the default
  compression dimension BUT you can choose a different
  compression dimension
• Essbase helps you choose a compression dimension by
  estimating what the database size would be depending on
  which dimension is tagged as compression
• Time is an excellent candidate for compression dimension,
  especially if you have fiscal year as a separate dimension
Calculations
Aggregating ASO Data

•   For ASO databases, after data values are loaded into the level 0 cells of an
    outline, the database requires no separate calculation step
•   From any point in the database, users can retrieve and view values that are
    aggregated for only the current retrieval
•   ASO databases are smaller than block storage databases, enabling quick
    retrieval of data values
•   For even faster retrieval, you can precalculate data values and store the
    precalculated results in aggregations
Aggregations – The Down Side

•   Lengthy process
•   Requires extra disk space
     – Sometimes yes, but it is rare that default aggs are more than 40 percent the size
       of the input data.


•   You want to balance query time and storage space
Intelligent Aggregations for ASO

• You can define hard restrictions for a dimension
   – Default (no restriction for primary hierarchy, no aggregation for alternate
     hierarchies)
   – Consider all levels
   – Do not aggregate
   – Consider top level only
       • (you only query top level)
   – Never aggregate to intermediate levels
       • (you only query level zero or top dimension)
• Level based weighting – provide levels to consider
• Process
   – ASO considers hints when creating aggregations
   – Attempts to create the most useful aggregations based on
     hints
Query Hints

•   You can define ―soft restrictions‖ as a query hint
•   Just select a representative member (any member)
•   Essbase will take this into consideration when creating aggregation views
Query Hints
Design Considerations

•   BSO
    – Complex calculations and allocations
    – Write back at upper levels from end users are required
•   ASO
    – Large analysis applications with many dimensions and members
    – Rolling up and analyzing large volumes of data
•   Both ASO and BSO
    – Take advantage of the strengths of both database types
Consider Using Both ASO and BSO



    BSO               Budget




          Partition




           Product             ASO
             SKU
           Analysis
Consider Using Both ASO and BSO –
             version 11


        Product                  ASO
          SKU
        Analysis

            Partition




      BSO               Budget
ASO vs. BSO Recap

• What are the similarities between ASO and BSO?
   – Building dimensions
   – Loading data (level zero only for ASO)
   – Retrieving data
• What are the differences?
   –   Many dimensions, many members
   –   No calc scripts
   –   Use MDX member formulas
   –   Aggregations for improved query performance
• Lots of improvements in System 9 and version 11
   – Understand the limitations in early versions of Essbase ASO
   – Don‘t miss the new features in 9.3.1 and 11x
Thank You!!


        Edward Roske
     eroske@interrel.com
BLOG: LookSmarter.blogspot.com
  WEBSITE: www.interrel.com
      TWITTER: ERoske

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Unlocking the secrets to how essbase thinks e roske in sync10 oracle epm track

  • 1. Unlocking the Secrets to How Essbase Thinks Edward Roske eroske@interrel.com BLOG: LookSmarter.blogspot.com WEBSITE: www.interrel.com TWITTER: ERoske
  • 2. About interRel • 2008 & 2009 Oracle Titan Award winner - EPM Solution of the year • 2008 Oracle EPM Excellence Award • 2009 Oracle EPM/BI Innovation Award • One of the fastest growing companies in the world (Inc. Magazine, ‘08 & ‗09) • Two of the four Hyperion Oracle ACE Directors in the world • Oracle Gold Specialized Partner in Oracle EPM • Focused exclusively on Oracle Hyperion EPM software – Consulting – Training – Infrastructure and Installation – Support – Software sales
  • 3. • 5 Hyperion Books Available: – Essbase (7): Complete Guide – Essbase System 9: Complete Guide – Essbase System 9: End User Guide – Smart View 11: End User Guide – Essbase 11: Admin Guide – Hyperion Planning for End Users • Coming in March – Hyperion Planning for Administrators • To order, check out www.LuLu.com 3 Copyright © 2007, Hyperion. All rights reserved.
  • 4. Agenda • Introduction • Internal Workings of Essbase: BSO • Internal Workings of Essbase: ASO • Question and Answer
  • 5. Internal Workings of Essbase: Block Storage
  • 6. Bob Earle Invented ―Sparse and Dense‖ • How is data distributed? SPARSE DENSE Products Time Periods X X X X Accounts X X X X X X Locations X X X X X X X X X X X X X X X
  • 7. Block Structure Var% Var Bud Act UnitsSold Headcnt Profit Account (dense) Expense OtherExp Marketing Salary Rev COGs Sales Jan Feb Mar Q1 Q2 Q3 Yr Period (dense)
  • 8. Data Cells Within the Block Actual -> Profit -> Jan Cross-dimensional operator (means Actual BY Profit BY Jan)
  • 9. Blocks • An individual block is created for each combination of sparse stored members Cola->East Cola->New York Cola->Florida Cola->Massachusetts
  • 11. Storage • PAG File – Contains the data (the blocks with a header for each) – Contains up to 2 Gb of data in each PAG file (32-bit) – Can be 1,024 different files – Can be compressed and fragmented – Can be stored on multiple drive locations • IND File – Contains the list or pointers to the blocks (intersection of sparse dimensions) – Contains up to 2 Gb of index in each IND file (32-bit) – Can be 1,024 different files – Can be fragmented – Can be stored on multiple drive locations
  • 12. Compression • ―Simple‖ compression settings – None – zLib – Index Value Pair • Can‘t assign directly • Good for really large blocks with really sparse data • Following types use multiple compressions (one per block) – Bitmap • Good for non-repeating data • Will use Bitmap or IVP – RLE = Run Length Encoding • Good for data with zeros and data that repeats (such as budgeting) • Will use RLE, Bitmap, or IVP
  • 13. Dimension Order & Compression • Dimension order affects compression • First dense dimension determines your ―columns‖ in PAG file • Compression is from left to right, top to bottom • Move Time to first dense dimension and we get: BUDGET Jan Feb Mar Apr May Jun Sales 100 100 100 120 120 120 COGS 50 50 50 50 50 50 Margin 50 50 50 70 70 70 Exp. 30 30 30 30 30 30 Profit 20 20 20 40 40 40 • Notice repeating values • Time should be dense then Measures for RLE compression
  • 15. Calculation Process Accounts Jan Feb Mar Qtr1 Sales 124.71 119.43 161.93 406.07 COGS 42.37 38.77 47.28 128.42 Margin 82.34 80.66 114.65 277.65
  • 16. Dense Calculation After calc of Time Year dimension Qtr1 Data load from table XXXX XXXXXXX Jan After calc of XX ### Accounts XX ### Feb dimension XX ### Mar Sales COGS Margin Profit
  • 17. Sparse Calculation East -> Cola  Calculated blocks  Upper-level blocks  Input blocks  Level zero blocks Vermont -> Cola New York -> Cola
  • 18. Calculation Order: All Dimensions • First, Accounts • Second, Time • Third, remaining dense dimensions • Fourth, remaining sparse dimensions • Two Pass Calculation
  • 19. Calculations • Default Calc - simplest method • Calc scripts • Dynamic calculations • Intelligent calculation
  • 20. Commit Blocks • Using Uncommitted Access – When Commit Level is reached, blocks write to hard drive • Default is 3000 blocks • Setting Commit Blocks to Zero – Writes at completion of the entire transaction – Will dramatically improve calculation time – Will fragment your PAG file during a calculation
  • 22. Index Cache Index pages New requests Index pages on in physical disk index cache Old requests Memory Disk
  • 23. Data Cache New requests Disk blocks on Disk blocks in physical disk data cache Old requests Memory Disk
  • 24. Internal Workings of Essbase: Aggregate Storage
  • 25. BSO Limitations • Financial applications are more densely populated so BSO works great in those instances • BSO engine can handle sparse data but on a ―limited‖ scale • Outline size limited • Batch times required for loads and calcs
  • 26. Aggregate Storage Option • Remember all the concepts we just learned: – Dense / Sparse – Index / page files X – Cache settings – Block storage
  • 27. Aggregate Storage Databases • Similar to a BSO database – outline, dimensions, hierarchies… BUT • Different method for storing/calculating databases • New storage kernel built to handle ASO databases • Calculates 10-100x faster • Stores up to 252 dimension combinations
  • 28. Aggregate Storage Databases • ASO addresses the following types of databases: – Read-only* • Write back to level zero available in 9.3.1 – ―Rack and stack‖ – Large dimensions • New Types of databases are possible – Customer analysis—data is analyzed from any dimension and there are potentially millions of customers – Procurement analysis—many products are tracked across many vendors – Logistics analysis—near real-time updates of product shipments – Market Basket analysis—products purchased along with other products
  • 29. When to use ASO • Database is sparse and has many dimensions, and/or the dimensions have many levels of members • Database is used primarily for read-only purposes, with few or no data updates • Calculation of the database is frequent, is based mainly on summarizing of the data, and does not rely on calculation scripts • Starting in Essbase 11x, ASO should be your default/starting idea
  • 30. BSO vs. ASO BSO Outline ASO Outline
  • 31. End User Perspective • End users won‘t care whether their database is ASO or BSO • The way users access ASO is the same as BSO – Excel Add-in – Financial Reporting, Web Analysis, etc. – Smart View Add-in • Repeat… no differences (just more data/dimensions)
  • 32. ASO Benefits from IT Perspective • Faster load and calc times provide – Lower hardware costs – Lower maintenance costs – Higher availability • Small disk footprints • Efficient tuning for storage and query response
  • 33. How does ASO work? • Simple question with not so simple answer • Asked greatest minds in the business how ASO works and got the same resounding answer: • ―It‘s a black box‖ or ―it's top secret and hard to understand‖ • There must be a better answer!
  • 34. ASO is Designed For… • More dimensions and members • Less time required for batches – Fast aggregation of sparse data sets – Faster loads – Incremental loads • Reduction in database footprint
  • 35. Key Concepts • Storage • Sparse data • Indexing • Aggregation • Nodes
  • 36. ASO Storage (ROLAP in disguise) • ASO can also be said to be ―ROLAP with a super fancy index scheme that rules.‖ • Big difference between ASO and BSO and ROLAP is the ASO storage mechanism • ROLAP stores data in a table and indexes a combination key between the rows • Essbase stores the concept of a cube of data in multiple dimensions or rather multiple keys
  • 37. ASO Storage (ROLAP in disguise) • It‘s a multidimensional index • ASO takes it a step further and indexes the indexes in a way for more rapid aggregation of data • Storage is no longer in "blocks" but in highly optimized aggregation nodes • Visualize it as an asymmetric fractal Christmas tree flattened out and then indexed again
  • 38. How does ASO work • Load Data only at level 0 • Create aggregate views • Algorithm selects and stores ―most taxing‖ queries • Dynamic queries at runtime and increased speed by leveraging nearest stored view
  • 39. ASO Concepts • Concept of stored and dynamic hierarchies – Stored hierarchies can only aggregate – Dynamic hierarchies can utilize formulas and advanced unary operators • Formulas on dimension members use MDX syntax • Pre-aggregated views can be defined to help query performance • Aggregated design wizard helps you create aggregation scripts • Outline paging helps you page portions of the outline in and out of memory to assist in performance • You can convert BSO outlines to ASO outlines using a wizard
  • 41. Directory Structure • Directory structure differs in both content and purpose from BSO • Tablespaces are utilized to store data and metadata – Default – stores numeric data (.dat file) – Log – records database activity – Metadata – stores metadata information about the objects in the database – Temp – temporary working space for the Essbase kernel
  • 42. Tablespace Overview • Defines the database storage in the form of file locations • Each ASO application has 4 tablespaces – Default – database values – Temp – temporary work space – Log – transaction log files – Metadata – database data structure • File location specifies a physical disk space for storing database files • Each tablespace may contain one or more file locations – Can span multiple physical drives and/or logical volumes
  • 44. Sizing Tablespaces • During the data load and aggregation process, data is stored in both the Temp and Default directories • ASO will always build the full .dat file in the temp tablespace while the default tablespace still has the production .dat • Hence, for your maximum drive size you have to plan on AT LEAST 3x your max bloated .dat size if you want to be "safe" (buffer to temp to default)
  • 45. Compression Dimension ASO • In old releases, the Accounts dimension enabled database compression • Beginning in 9.3, the Accounts dimension is the default compression dimension BUT you can choose a different compression dimension • Essbase helps you choose a compression dimension by estimating what the database size would be depending on which dimension is tagged as compression • Time is an excellent candidate for compression dimension, especially if you have fiscal year as a separate dimension
  • 47. Aggregating ASO Data • For ASO databases, after data values are loaded into the level 0 cells of an outline, the database requires no separate calculation step • From any point in the database, users can retrieve and view values that are aggregated for only the current retrieval • ASO databases are smaller than block storage databases, enabling quick retrieval of data values • For even faster retrieval, you can precalculate data values and store the precalculated results in aggregations
  • 48. Aggregations – The Down Side • Lengthy process • Requires extra disk space – Sometimes yes, but it is rare that default aggs are more than 40 percent the size of the input data. • You want to balance query time and storage space
  • 49. Intelligent Aggregations for ASO • You can define hard restrictions for a dimension – Default (no restriction for primary hierarchy, no aggregation for alternate hierarchies) – Consider all levels – Do not aggregate – Consider top level only • (you only query top level) – Never aggregate to intermediate levels • (you only query level zero or top dimension) • Level based weighting – provide levels to consider • Process – ASO considers hints when creating aggregations – Attempts to create the most useful aggregations based on hints
  • 50. Query Hints • You can define ―soft restrictions‖ as a query hint • Just select a representative member (any member) • Essbase will take this into consideration when creating aggregation views
  • 52. Design Considerations • BSO – Complex calculations and allocations – Write back at upper levels from end users are required • ASO – Large analysis applications with many dimensions and members – Rolling up and analyzing large volumes of data • Both ASO and BSO – Take advantage of the strengths of both database types
  • 53. Consider Using Both ASO and BSO BSO Budget Partition Product ASO SKU Analysis
  • 54. Consider Using Both ASO and BSO – version 11 Product ASO SKU Analysis Partition BSO Budget
  • 55. ASO vs. BSO Recap • What are the similarities between ASO and BSO? – Building dimensions – Loading data (level zero only for ASO) – Retrieving data • What are the differences? – Many dimensions, many members – No calc scripts – Use MDX member formulas – Aggregations for improved query performance • Lots of improvements in System 9 and version 11 – Understand the limitations in early versions of Essbase ASO – Don‘t miss the new features in 9.3.1 and 11x
  • 56. Thank You!! Edward Roske eroske@interrel.com BLOG: LookSmarter.blogspot.com WEBSITE: www.interrel.com TWITTER: ERoske