<Insert Picture Here> Implementing Efficient Essbase ASO Application
|
|
- Lynn Hensley
- 6 years ago
- Views:
Transcription
1
2 <Insert Picture Here> Implementing Efficient Essbase ASO Application Buland Chowdhury & Steve Liebermensch Senior Technical Director
3 Agenda Basic Design Dimension settings and usage Partitioning Formulas MDX Optimization Tuning Calculation Aggregation Materialization Data Load Others
4 The following is intended to outline our general guideline. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions.
5 Basic Design An Essbase model needs to have a focus area or needs to be subject oriented. For example if you are creating a customer analysis application stay focused on elements that are relevant to customer analysis. Don t introduce additional elements in to the model just because ASO allows it. Lack of focus or subject orientation can lead to inter dimensional irrelevance. Always avoid it. Inter dimensional irrelevance occurs when many members of a dimension are irrelevant across other dimensions
6 Basic Design Don t design an ASO model as if it is a relational database. ASO s capability has increased significantly but it is still an OLAP application not a Relational Database.
7 Basic Design Theoretically there is no limit on the number of dimensions that an ASO model can have. That should not be an excuse to allow dimension into the model. Don t introduce a dimension just in case some one needs it. Focus should be relevant to subject area and frequency of use. Utilize drill through functionality where elements are not directly tied to subject area but relevant for analysis or less frequently used dimensions.
8 Dimension settings and usage Compression ASO applications can be very large. For this reason, compression becomes very important. Following are the things that can be done to improve compression Make sure one dimension is selected as a compression dimension Select a dimension with a higher probability of data intersection with a lower number of level 0 members If possible change the member ordering so that a member with higher data intersection comes first Round number to 2 decimal points. If data value have more than 2 decimal point values are save as is (no compression) Ideal compression is achieved if the stored leaf level member count is evenly divisible by 16
9 Dimension Settings and Usage Accounts Dynamic dimension Allows for non additive unary operators Required for time balance functionality Expense flags are accomplished through UDAs and member formulae In the case of large GL type accounts dimensions should consider storing the dimension
10 Dimension Settings and Usage Accounts Must be a dynamic dimension due to - unary operator on COGS and Total Expenses
11 Dimension Settings and Usage Accounts Simplest hierarchy and allows for maximum stored aggregates. Leverages load rule setting of sign flip on UDA. Requires the user to accept different signage of values.
12 Dimension Settings and Usage Accounts Preserves original signage and allows for some aggregates to be stored
13 Dimension Settings and Usage Time Can make a good candidate for compression dimension provided no time balance requirements Must be stored for time balance feature Use multi-hierarchy dimension if formulae are necessary To-date formulae Prior to best if performed in Time dimension and forward, most functional to use analytic dimension
14 Dimension Settings and Usage Time Use analytic dimension if you need perform time calculations like period to date sums, period over period comparisons Use AGGREGATE function if your period to date calculations need to take Time Balance tags into account ORACLE CONFIDENTIAL
15 Dimension Settings and Usage Time You can write the AGGREGATE function as follows. It some time improves the retrieval performance YTD: AGGREGATE(PeriodsToDate([Time].Generations(2), [Time].CurrentMember), [Base]) QTD: AGGREGATE(PeriodsToDate([Time].Generations(3), [Time].CurrentMember), [Base]) Where [Base] is the stored member in Analytic dimension called TimeView dimension, where you load data at
16 Partitioning Execution across multiple partitions is performed serially Performance will be impacted by volume of data being transferred across the partition Query tracking can and should be done against the source model via the partition BSO as a source should be used only with forethought Formulae on the target model should only be written for members where the calculation crosses partitions
17 Partitioning Target tuning Perform all calculations possible on the source(s)
18 Formulas Simplify complex formulas using intermediate formulas Use shortest possible MDX idioms/expressions Progressively eliminate cells needing to be processed for a formula Avoid CURRENTMEMBER references wherever possible Use specific MDX functions/expression patterns that are internally optimized Use built-in calculations (Time Balance, Ranking calculations) ORACLE CONFIDENTIAL
19 MDX Optimization Meta-data vs data Performance difference between the following statements: IIf(Scenario.CurrentMember = Actual,.) Or IIf(Is(Scenario.CurrentMember,Actual),.)
20 Optimized MDX Functions Use the following functions instead of using PARENT, CHILD, LEAD and LAG to navigate a hierarchy OpeningPeriod ClosingPeriod ParallelPeriod LastPeriods PeriodsToDate FirstChild LastChild FirstSibling LastSibling MemberRange RelMemberRange
21 MDX Optimization Goal is to calculate a product s 3 month average Transactions contribution % to [All Merchandise] 3 month average:
22 MDX Optimization Round( ( ( ([Time].CurrentMember,[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(1),[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(2),[Products].CurrentMember,[Transactions]) ) / (([Time].CurrentMember,Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(1),Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(2),Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) ) ),4)
23 MDX Optimization Round( ( ( ([Time].CurrentMember,[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(1),[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(2),[Products].CurrentMember,[Transactions]) Remove unnecessary ) / functions. Formatting (([Time].CurrentMember,Ancestor should (Products.CurrentMember, left to the Products.Generations(2)),[Transactions]) reporting tool. + ([Time].CurrentMember.Lag(1),Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(2),Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) ) ),4)
24 MDX Optimization ( ( ([Time].CurrentMember,[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(1),[Products].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(2),[Products].CurrentMember,[Transactions]) ) / (([Time].CurrentMember,Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(1),Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(2),Ancestor Avoid (Products.CurrentMember, unnecessary references Products.Generations(2)),[Transactions]) ) to ) CurrentMember. It is assumed without explicitly using a function
25 MDX Optimization ( ( ([Time].CurrentMember,[Transactions]) + ([Time].CurrentMember.Lag(1),[Transactions]) + ([Time].CurrentMember.Lag(2), [Transactions]) ) / (([Time].CurrentMember,Ancestor (Products.CurrentMember, Products.Generations(2)),[Transactions]) + ([Time].CurrentMember.Lag(1),Ancestor (Products.CurrentMember, Simplify formulae, no need to Products.Generations(2)),[Transactions]) use 5 functions + where 1 will ([Time].CurrentMember.Lag(2),Ancestor do. 3 CurrentMember (Products.CurrentMember, functions and 2 Lag Products.Generations(2)),[Transactions]) ) ) functions
26 MDX Optimization Sum(LastPeriods(3),Transactions)/ Sum(LastPeriods(3), (Ancestor (Products.CurrentMember, Products.Generations(2)), Transactions)) Don t use a function where direct referencing can be performed. In this model Gen 2 of the Products dimension will always be the same member
27 MDX Optimization Sum(LastPeriods(3),Transactions)/ Sum(LastPeriods(3),([All Merchandise], Transactions))
28 MDX Optimization Only perform calculation when data to support the math exists Case When Not IsEmpty(Sum(LastPeriods(3),Transactions)) Then Sum(LastPeriods(3),Transactions)/ Sum(LastPeriods(3), ([All Merchandise],Transactions)) End
29 MDX Optimization Additionally you should break up the complex formula using an additional member: Create a new member [Last 3 Month Transactions] Assign the formula: NonEmpty directive, Sum(LastPeriods(3),Transactions) feature Then the new % Contribution formula becomes: NonEmptyMember [Last 3 Month Transactions] [Last 3 Month Transactions] / ([Last 3 Month Transactions],[All Merchandise])
30 Tuning Calculation Using NON EMPTY Behavior ASO supports association of NON EMPTY behavior properties with formulas in 11.1.x Non empty behavior takes advantage of sparse nature of input data to reduce amount of dynamic calculations Non empty behavior can be defined by adding the following directives before a formula NONEMPTYMEMBER <memberlist> NONEMPTYTUPLE <tuple> ORACLE CONFIDENTIAL
31 Tuning Calculation Using NON EMPTY Behavior The measure [Units] is a loaded value The measure [Avg. Units/Transaction] is calculated using formula [Avg. Units/Transaction] = [Units]/[Transactions] The value of [Avg. Units/Transaction] is empty if value of [Units] is empty ORACLE CONFIDENTIAL
32 Tuning Calculation Using NON EMPTY Behavior The calculation of [Avg. Units/Transaction] can be skipped if value of [Units] is empty We can add non empty behavior for [Avg. Units/Transaction] by rewriting the formula as [Avg. Units/Transaction] = NONEMPTYMEMBER [Units] [Units]/[Transactions] The saving obtained from defining non empty behaviors can be very significant. Savings increase with the complexity of the formula and sparseness of the data. ORACLE CONFIDENTIAL
33 Examples of NON EMPTY behavior [AvgProdTranscation] is empty if [Transactions] for current Product is empty [Measures].[AvgProdTransactions] = NONEMPTYMEMBER [Transactions] AVG(Product.CurrentMember.Children, [Transactions]) [YTDSales] is empty if Sales for entire [Year] is empty [Measures].[YTDSales] = NONEMPTYTUPLE (Year,Sales) Sum(PeriodsToDate(Time.Years, Time.CurrentMember), Sales) ORACLE CONFIDENTIAL
34 Guidelines for Assigning NON EMPTY Behavior If you can identify a member such that a formula is empty if the value of that member is empty use NONEMPTYMEMBER directive with that member The members in NONEMPTYMEMBER should be from the same dimension as the formula member to which you apply this directive. Use multiple members in NONEMPTYMEMBER if one member is not sufficient Use NONEMPTYTUPLE if you need members from multiple dimensions to indicate non empty behavior ORACLE CONFIDENTIAL
35 Use LEAVES and NONEMPTYSUBSET functions For large dimensions (over 100,000 members) use LEAVES function instead of using DESCENDANTS to get leaf level members. LEAVES returns only leaf level members that have non-empty data Leaves(Products) Use NONEMPTYSUBSET to reduce size of your query NonEmptySubset(Crossjoin(Leaves(Customers), Leaves(Products), Sales) ORACLE CONFIDENTIAL
36 Dynamic Calculations Log Messages Application log has some informative messages related to dynamic calculations that can be helpful in tuning The following message indicates an error in a formula. Fix the error message or drop these members from outline if they are not required Error encountered when loading member [<MemberName>]'s calc string [<FormulaExpression>], ignored The following message indicates invalid solve order assignment. Assign correct solve orders for members indicated in the message Invalid formula for member [<A>] (reason: depends on a member [<B>] with higher solveorder ) will be ignored during execution ORACLE CONFIDENTIAL
37 Dynamic Calculations Log Messages The following message indicate that a formula is too complex for Essbase to optimize. Rewrite if possible. Formula for member [% of Total] will be executed in [CELL] mode The following message indicates that a formula is complex and Essbase couldn t detect a non empty behavior for it. The formula for member [MemberName] is Complex. If possible, add a non-empty directive to optimize for sparse data ORACLE CONFIDENTIAL
38 ASO Aggregation Use parallel calculation. Set CALCPARALLEL setting to number of CPU available. Increase ASO cache size higher than the what you have during data load to accommodate parallel calculation. You can use Maxl to accomplish this. Depending on situation a full database aggregation may not be optimal. Optimizing aggregations may require an iterative, fine-tuning process. Query tracking probably the most efficient way to decide what to aggregate, provided queries reflect the users data need properly; However, if you are knowledgeable about user and their query need you can use User-Defined View Selection where you will be able to select top level, bottom level in secondary hierarchy and many others options. Use hierarchy aggregation settings in the outline editor
39 Materialization Query hints On Dimension/Hierarchy
40 Materialization By default Aggregate Storage Option is parallel threaded with two threads, can use up to 8 When loading incremental data, aggregate views are also updated Materialization scripts can be saved and reused to eliminate regeneration of query tracked results The database can be available during materialization without data consistency issues
41 Materialization Query hints Dimensional intersections
42 ASO Query Aggregation and query are related items. Most of the items that we have discussed in previous slides will improve query performance A hint can be used to optimize heavily queried level combinations. Avoid creating too many hints, you will lose effectiveness.
43 Data Load Behavior Within a buffer duplicate records are additive irrelevant of load rule settings When committing the buffer it adheres to data load rule setting related to overwrite, add to, or subtract from Single threaded, can use DLTHREADS cfg settings but thorough testing should be performed Materialized views are updated as part of the data load process during an incremental update
44 Data Load Slice loading Is the primary feature enabling Excel based data submission and trickle feeding functionality Creates subcubes alongside the primary slice of the database Dynamic aggregations are performed across the necessary slices to provide query results Different materialized views might exist within a slice as compared to the primary slice of the database
45 Data Load Optimization Compression dimension should be across the load file Consider removing aggregate views prior to an incremental update of data Data should be loaded as additive values Multiple buffers can be used to parallel load the database. Requires simultaneous Maxl processes to be executed Ignore Zeros and Missing values whenever possible, maxl buffer setting parameter
46 Data Load Optimization Don t load 0 (zero) values if possible Use ASO cache setting. The number is based on the number of cells that would be loaded. While setting ASO cache try not to exceed 25% of the available physical memory to leave more space for the OS file cache For a Database with 1 billion cells or more, the cache size may be set as high as 512 MB or 1 GB if the available memory permits it Avoid (if possible) Text substitutions, field splits/joins and white space truncation in rule file
47 ASO Others If possible allocate space for default and temp table spaces on different physical disk devices where applicable
48
49
<Insert Picture Here> Optimizing ASO
Optimizing ASO Steve Liebermensch Consulting Technical Director The following is intended to outline our general product direction. It is intended for information purposes only, and
More informationTop 10 Essbase Optimization Tips that Give You 99+% Improvements
Top 10 Essbase Optimization Tips that Give You 99+% Improvements Edward Roske info@interrel.com BLOG: LookSmarter.blogspot.com WEBSITE: www.interrel.com TWITTER: Eroske 3 About interrel Reigning Oracle
More informationKSCOPE11.COM/BIEPM. USE THE SPECIAL interrel CODE IRC TO RECEIVE A $100 DISCOUNT ON REGISTRATION
Oracle BI & EPM Tracks Essbase Hyperion Planning & HFM Beginner to Guru Content OBIEE 11g Hyperion Developers On-Site CONFERENCE HIGHLIGHTS 250+ Sessions Hands-on Training Six full-day Symposiums CHECK
More informationLearning about MDX. Pako Chan, CPA, CA, CITP Manager, Business Solutions Group
Learning about MDX Pako Chan, CPA, CA, CITP Manager, Business Solutions Group Introduction Pako Chan Manager, Business Solutions Group CPA-CA, CITP Auditor Deloitte 5 years Architect Prophix 6 years Agenda
More informationQuestion No : 2 Identify four disadvantages / considerations when using a transparent partition.
Volume: 69 Questions Question No : 1 Which two are Essbase components? A. Essbase server B. Administration services C. C API D. Web Analysis E. Financial reporting Answer: A,B Question No : 2 Identify
More informationHFM Extended Analytics Integration with ASO Essbase. Speakers: Brian Marshall Jon Rambeau
HFM Extended Analytics Integration with ASO Essbase Speakers: Brian Marshall Jon Rambeau Agenda Why HFM to Essbase? Why ASO Instead of BSO? Getting Data Out of HFM. Customer Needs. Needs and Challenges.
More informationHow Oracle Essbase Aggregate Storage Option. And How to. Dan Pressman
How Oracle Essbase Aggregate Storage Option And How to Dan Pressman San Francisco, CA October 1, 2012 Assumption, Basis and a Caveat Assumption: Basic understanding of ASO cubes Basis: My chapter How ASO
More informationIDU0010 ERP,CRM ja DW süsteemid Loeng 5 DW concepts. Enn Õunapuu
IDU0010 ERP,CRM ja DW süsteemid Loeng 5 DW concepts Enn Õunapuu enn.ounapuu@ttu.ee Content Oveall approach Dimensional model Tabular model Overall approach Data modeling is a discipline that has been practiced
More informationChapter 13: Query Processing
Chapter 13: Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions 13.1 Basic Steps in Query Processing 1. Parsing
More informationAdvanced Database Systems
Lecture IV Query Processing Kyumars Sheykh Esmaili Basic Steps in Query Processing 2 Query Optimization Many equivalent execution plans Choosing the best one Based on Heuristics, Cost Will be discussed
More informationOBIEE Performance Improvement Tips and Techniques
OBIEE Performance Improvement Tips and Techniques Vivek Jain, Manager Deloitte Speaker Bio Manager with Deloitte Consulting, Information Management (BI/DW) Skills in OBIEE, OLAP, RTD, Spatial / MapViewer,
More information! A relational algebra expression may have many equivalent. ! Cost is generally measured as total elapsed time for
Chapter 13: Query Processing Basic Steps in Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions 1. Parsing and
More informationChapter 13: Query Processing Basic Steps in Query Processing
Chapter 13: Query Processing Basic Steps in Query Processing! Overview! Measures of Query Cost! Selection Operation! Sorting! Join Operation! Other Operations! Evaluation of Expressions 1. Parsing and
More informationChapter 12: Query Processing. Chapter 12: Query Processing
Chapter 12: Query Processing Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 12: Query Processing Overview Measures of Query Cost Selection Operation Sorting Join
More informationDatabase System Concepts
Chapter 13: Query Processing s Departamento de Engenharia Informática Instituto Superior Técnico 1 st Semester 2008/2009 Slides (fortemente) baseados nos slides oficiais do livro c Silberschatz, Korth
More informationHow Oracle Essbase Aggregate Storage Option
How Oracle Essbase Aggregate Storage Option and how to Dan Pressman Dan.Pressman@ntuple.net Blog: TheEssbaseMechanic@wordpress.com www.ntuple.net Jun 24, 2014 Seattle, WA Warning Danger! The Information
More informationChapter 12: Query Processing
Chapter 12: Query Processing Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Overview Chapter 12: Query Processing Measures of Query Cost Selection Operation Sorting Join
More informationOracle Essbase XOLAP and Teradata
Oracle Essbase XOLAP and Teradata Steve Kamyszek, Partner Integration Lab, Teradata Corporation 09.14 EB5844 ALLIANCE PARTNER Table of Contents 2 Scope 2 Overview 3 XOLAP Functional Summary 4 XOLAP in
More informationBudget Process Tools: Smart View Ad Hoc Basics 1
Budget Process Tools: Smart View Ad Hoc Basics 1 10.27.16 CalPlan HCP (Human Capital Planning) CalRptg HCPRptg Smart View 1 An ad hoc committee What is your particular purpose for querying into your CalPlanning
More informationQuery Processing. Debapriyo Majumdar Indian Sta4s4cal Ins4tute Kolkata DBMS PGDBA 2016
Query Processing Debapriyo Majumdar Indian Sta4s4cal Ins4tute Kolkata DBMS PGDBA 2016 Slides re-used with some modification from www.db-book.com Reference: Database System Concepts, 6 th Ed. By Silberschatz,
More informationHANA Performance. Efficient Speed and Scale-out for Real-time BI
HANA Performance Efficient Speed and Scale-out for Real-time BI 1 HANA Performance: Efficient Speed and Scale-out for Real-time BI Introduction SAP HANA enables organizations to optimize their business
More informationQuery Processing & Optimization
Query Processing & Optimization 1 Roadmap of This Lecture Overview of query processing Measures of Query Cost Selection Operation Sorting Join Operation Other Operations Evaluation of Expressions Introduction
More informationBudget Process Tools: Smart View Ad Hoc Basics 1
Budget Process Tools: Smart View Ad Hoc Basics 1 01.12.17 CalPlan HCP (Human Capital Planning) CalRptg HCPRptg Smart View 1 An ad hoc committee What is your particular purpose for querying into your CalPlanning
More informationMSBI Online Training (SSIS & SSRS & SSAS)
MSBI Online Training (SSIS & SSRS & SSAS) Course Content: SQL Server Integration Services Introduction Introduction of MSBI and its tools MSBI Services and finding their statuses Relation between SQL Server
More informationTip or Technique. OLAP Functions. Product(s): Cognos 8 BI Area of Interest: Report Design
Tip or Technique OLAP Functions Product(s): Cognos 8 BI Area of Interest: Report Design OLAP Functions 2 Copyright Your use of this document is subject to the Terms of Use governing the Cognos software
More informationOne of the prevailing business drivers that have
a hyperion white paper non unique member names: an easier way to build analytic applications One of the prevailing business drivers that have helped propel MultiDimensional technologies as the preferred
More informationOracle Hyperion Tips and Tricks. NEOAUG Eric Sanders, Gordon Strodel Monday, October 22, 2012
Oracle Hyperion 11.1.2.2 Tips and Tricks NEOAUG Eric Sanders, Gordon Strodel Monday, October 22, 2012 Agenda About Archetype What s New in 11.1.2.2: New User Interface Calculation Manager Manage Substitution
More informationNicholas Dritsas Principal Program Manager Microsoft Corporation Microsoft Corporation. All rights reserved
Nicholas Dritsas Principal Program Manager Microsoft Corporation Who is SQL Customer Advisory Team (SQL CAT) Overview of large AS projects Lessons Learned People and Infrastructure Performance Improving
More informationChapter 1 Readme.doc definitions you need to know 1
Contents Foreword xi Preface to the second edition xv Introduction xvii Chapter 1 Readme.doc definitions you need to know 1 Sample data 1 Italics 1 Introduction 1 Dimensions, measures, members and cells
More informationBudget Process Tools: Smart View Ad Hoc Basics
Budget Process Tools: Smart View Ad Hoc Basics 2 6.21.17 CalPlan HCP (Human Capital Planning) CalRptg HCPRptg Smart View 1 Agenda 1 2 3 4 5 6 Smart View Ad Hoc Basics 1 Homework Review Creating Multiple
More informationData Warehousing and Data Mining
Data Warehousing and Data Mining Lecture 3 Efficient Cube Computation CITS3401 CITS5504 Wei Liu School of Computer Science and Software Engineering Faculty of Engineering, Computing and Mathematics Acknowledgement:
More informationIntroduction to Query Processing and Query Optimization Techniques. Copyright 2011 Ramez Elmasri and Shamkant Navathe
Introduction to Query Processing and Query Optimization Techniques Outline Translating SQL Queries into Relational Algebra Algorithms for External Sorting Algorithms for SELECT and JOIN Operations Algorithms
More informationImproving the Performance of OLAP Queries Using Families of Statistics Trees
Improving the Performance of OLAP Queries Using Families of Statistics Trees Joachim Hammer Dept. of Computer and Information Science University of Florida Lixin Fu Dept. of Mathematical Sciences University
More information<Insert Picture Here> Fast Delivery of Intelligent BI Solutions
Fast Delivery of Intelligent BI Solutions Marty Gubar Oracle BI/DW Product Management Agenda The Requirement: Intelligent Ad hoc Data Exploration Overview: Embedded OLAP in Oracle
More informationSSAS Multidimensional vs. SSAS Tabular Which one do I choose?
SSAS Multidimensional vs. SSAS Tabular Which one do I choose? About Alan Sr BI Consultant Community Speaker Blogs at FalconTekSolutionsCentral.com SSAS Maestro Will work for cupcakes Generally speaks on
More informationOracle BI, Oracle OLAP, Essbase The Benefits and Cost of Openness. Collaborate 2008 paper 207. April 14, 2008
Oracle BI, Oracle OLAP, Essbase The Benefits and Cost of Openness Collaborate 2008 paper 207 April 14, 2008 Dan Vlamis, President, Vlamis Software Solutions, Inc. Agenda Introduction Perspective on Oracle
More informationStorage hierarchy. Textbook: chapters 11, 12, and 13
Storage hierarchy Cache Main memory Disk Tape Very fast Fast Slower Slow Very small Small Bigger Very big (KB) (MB) (GB) (TB) Built-in Expensive Cheap Dirt cheap Disks: data is stored on concentric circular
More informationEssbase Tips & Tricks. Webinar will Start at 12:05pm Eastern
Essbase Tips & Tricks Webinar will Start at 12:05pm Eastern 10.24.14 Alex Ladd OAUG Hyperion SIG Essbase Domain Lead 15 years of Planning/Essbase Experience 17 years of BI Experience Senior Partner - MindStream
More informationHyperion Essbase Audit Logs Turning Off Without Notification
Hyperion Essbase Audit Logs Turning Off Without Notification Audit logs, or SSAUDIT, are a crucial component of backing up Hyperion Essbase applications in many environments. It is the equivalent of a
More informationPentaho and Online Analytical Processing (OLAP)
Pentaho and Online Analytical Processing (OLAP) Change log (if you want to use it): Date Version Author Changes Contents Overview... 1 Before You Begin... 1 Use Case: Schema Creation... 1 Schema Setup...
More informationPeopleTools 8.51 PeopleBook: PeopleSoft Cube Manager
PeopleTools 8.51 PeopleBook: PeopleSoft Cube Manager August 2010 PeopleTools 8.51 PeopleBook: PeopleSoft Cube Manager SKU pt8.51tcbm-b0810 Copyright 1988, 2010, Oracle and/or its affiliates. All rights
More informationSegregating Data Within Databases for Performance Prepared by Bill Hulsizer
Segregating Data Within Databases for Performance Prepared by Bill Hulsizer When designing databases, segregating data within tables is usually important and sometimes very important. The higher the volume
More informationThe FDM adapter for Essbase is a target adapter that allows users to load data from FDM into Essbase.
Oracle Hyperion Financial Data Quality Management, Fusion Edition Adapter for Hyperion Essbase Release G4-H Readme Purpose... 1 Overview... 1 Installation... 2 Configuration... 2 Configuration Options...
More informationFileLoader for SharePoint
Administrator s Guide FileLoader for SharePoint v. 2.0 Last Updated 6 September 2012 Contents Preface 3 FileLoader Users... 3 Getting Started with FileLoader 4 Configuring Connections to SharePoint 8
More informationProceedings of the IE 2014 International Conference AGILE DATA MODELS
AGILE DATA MODELS Mihaela MUNTEAN Academy of Economic Studies, Bucharest mun61mih@yahoo.co.uk, Mihaela.Muntean@ie.ase.ro Abstract. In last years, one of the most popular subjects related to the field of
More informationMDX Quick Start Workshop
MDX Quick Start Workshop Contents IMPORTANT: BEFORE THE CLASS... 1 MDX EXERCISES... 3 Exercise 1: Basic MDX Queries... 3 Exercise 2: Tuples and Sets... 4 Exercise 3: Useful Properties and Functions...
More informationSQL Server Analysis Services
DataBase and Data Mining Group of DataBase and Data Mining Group of Database and data mining group, SQL Server 2005 Analysis Services SQL Server 2005 Analysis Services - 1 Analysis Services Database and
More informationSAMPLE. Preface xi 1 Introducting Microsoft Analysis Services 1
contents Preface xi 1 Introducting Microsoft Analysis Services 1 1.1 What is Analysis Services 2005? 1 Introducing OLAP 2 Introducing Data Mining 4 Overview of SSAS 5 SSAS and Microsoft Business Intelligence
More informationThis presentation covers Gen Z Buffer operations. Buffer operations are used to move large quantities of data between components.
This presentation covers Gen Z Buffer operations. Buffer operations are used to move large quantities of data between components. 1 2 Buffer operations are used to get data put or push up to 4 GiB of data
More informationOracle Hyperion Profitability and Cost Management
Oracle Hyperion Profitability and Cost Management Configuration Guidelines for Detailed Profitability Applications November 2015 Contents About these Guidelines... 1 Setup and Configuration Guidelines...
More informationIndexing. Jan Chomicki University at Buffalo. Jan Chomicki () Indexing 1 / 25
Indexing Jan Chomicki University at Buffalo Jan Chomicki () Indexing 1 / 25 Storage hierarchy Cache Main memory Disk Tape Very fast Fast Slower Slow (nanosec) (10 nanosec) (millisec) (sec) Very small Small
More informationAlgorithms for Query Processing and Optimization. 0. Introduction to Query Processing (1)
Chapter 19 Algorithms for Query Processing and Optimization 0. Introduction to Query Processing (1) Query optimization: The process of choosing a suitable execution strategy for processing a query. Two
More informationOracle Database 12c: JMS Sharded Queues
Oracle Database 12c: JMS Sharded Queues For high performance, scalable Advanced Queuing ORACLE WHITE PAPER MARCH 2015 Table of Contents Introduction 2 Architecture 3 PERFORMANCE OF AQ-JMS QUEUES 4 PERFORMANCE
More informationColumn Stores vs. Row Stores How Different Are They Really?
Column Stores vs. Row Stores How Different Are They Really? Daniel J. Abadi (Yale) Samuel R. Madden (MIT) Nabil Hachem (AvantGarde) Presented By : Kanika Nagpal OUTLINE Introduction Motivation Background
More informationOracle Essbase & Oracle OLAP: The Guide to Oracle's Multidimensional Solution by Michael Schrader et al. Oracle Press. (c) Copying Prohibited.
Oracle Essbase & Oracle OLAP: The Guide to Oracle's Multidimensional Solution by Michael Schrader et al. Oracle Press. (c) 2010. Copying Prohibited. Reprinted for Swapnil Ramkrishna Khirade, Accenture
More informationContents Contents Introduction Basic Steps in Query Processing Introduction Transformation of Relational Expressions...
Contents Contents...283 Introduction...283 Basic Steps in Query Processing...284 Introduction...285 Transformation of Relational Expressions...287 Equivalence Rules...289 Transformation Example: Pushing
More informationUsing SAP NetWeaver Business Intelligence in the universe design tool SAP BusinessObjects Business Intelligence platform 4.1
Using SAP NetWeaver Business Intelligence in the universe design tool SAP BusinessObjects Business Intelligence platform 4.1 Copyright 2013 SAP AG or an SAP affiliate company. All rights reserved. No part
More informationOracle Database In-Memory By Example
Oracle Database In-Memory By Example Andy Rivenes Senior Principal Product Manager DOAG 2015 November 18, 2015 Safe Harbor Statement The following is intended to outline our general product direction.
More informationCHAPTER 8: ONLINE ANALYTICAL PROCESSING(OLAP)
CHAPTER 8: ONLINE ANALYTICAL PROCESSING(OLAP) INTRODUCTION A dimension is an attribute within a multidimensional model consisting of a list of values (called members). A fact is defined by a combination
More informationsqlbi.com 1 Who am I Latest conferences Agenda sqlbi.com
presented by Marco Russo marco@ Who am I Latest conferences BI Experts and Consultants Problem Solving Complex Project Assistance DataWarehouse Assesments and Development Courses, Trainings and Workshops
More information1 of 8 14/12/2013 11:51 Tuning long-running processes Contents 1. Reduce the database size 2. Balancing the hardware resources 3. Specifying initial DB2 database settings 4. Specifying initial Oracle database
More informationCatalogue of requirements for a frontend tool on Oracle Essbase
good. better. outperform. Catalogue of requirements for a frontend tool on Oracle Essbase The best approach for finding the right tool by Michael Mühlena March 2017 Factors that may affect your choice
More informationDATA CUBE : A RELATIONAL AGGREGATION OPERATOR GENERALIZING GROUP-BY, CROSS-TAB AND SUB-TOTALS SNEHA REDDY BEZAWADA CMPT 843
DATA CUBE : A RELATIONAL AGGREGATION OPERATOR GENERALIZING GROUP-BY, CROSS-TAB AND SUB-TOTALS SNEHA REDDY BEZAWADA CMPT 843 WHAT IS A DATA CUBE? The Data Cube or Cube operator produces N-dimensional answers
More informationOAC Essbase Hybrid Block Storage Option Performance Tuning
OAC Essbase Hybrid Block Storage Option Performance Tuning Oracle Analytics Cloud (OAC) Essbase has a new Hybrid Calculation Engine that improves the overall performance of Essbase Applications and reduces
More informationInformatica Data Explorer Performance Tuning
Informatica Data Explorer Performance Tuning 2011 Informatica Corporation. No part of this document may be reproduced or transmitted in any form, by any means (electronic, photocopying, recording or otherwise)
More informationJyotheswar Kuricheti
Jyotheswar Kuricheti 1 Agenda: 1. Performance Tuning Overview 2. Identify Bottlenecks 3. Optimizing at different levels : Target Source Mapping Session System 2 3 Performance Tuning Overview: 4 What is
More informationManaging Smart View Shared Connections
Managing Smart View Shared Connections If you use Smart View, you are familiar with the Smart View Shared Connection URL, which is unique to the environment that Smart View connects. That property is saved
More informationExadata Implementation Strategy
Exadata Implementation Strategy BY UMAIR MANSOOB 1 Who Am I Work as Senior Principle Engineer for an Oracle Partner Oracle Certified Administrator from Oracle 7 12c Exadata Certified Implementation Specialist
More informationDimensionality & Dimensions of Hyperion Planning
Dimensionality & Dimensions of Hyperion Planning This tutorial will take you through the dimensionality concepts of Hyperion Planning. Dimensions are the basic foundation of the Hyperion Planning application
More informationThe Emergence of Application Logic Compilers Stefan Dipper, SAP BW Development Sept, Public
The Emergence of Application Logic Compilers Stefan Dipper, SAP BW Development Sept, 2013 Public Agenda What is an application logic compiler? Why stored procedures History Domain specific language - Why
More informationPerformance Innovations with Oracle Database In-Memory
Performance Innovations with Oracle Database In-Memory Eric Cohen Solution Architect Safe Harbor Statement The following is intended to outline our general product direction. It is intended for information
More informationHyperion Data Integration Management Adapter for Essbase. Sample Readme. Release
Hyperion Data Integration Management Adapter for Essbase Release 11.1.1.1 Sample Readme [Skip Navigation Links] Purpose... 2 About Data Integration Management Release 11.1.1.1... 2 Data Integration Management
More informationThe following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into
The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material,
More informationChapter 12: Indexing and Hashing. Basic Concepts
Chapter 12: Indexing and Hashing! Basic Concepts! Ordered Indices! B+-Tree Index Files! B-Tree Index Files! Static Hashing! Dynamic Hashing! Comparison of Ordered Indexing and Hashing! Index Definition
More informationFoster B-Trees. Lucas Lersch. M. Sc. Caetano Sauer Advisor
Foster B-Trees Lucas Lersch M. Sc. Caetano Sauer Advisor 14.07.2014 Motivation Foster B-Trees Blink-Trees: multicore concurrency Write-Optimized B-Trees: flash memory large-writes wear leveling defragmentation
More informationEvolution or Revolution the new Hybrid Essbase. Cameron Lackpour Tim German Dan Pressman
Evolution or Revolution the new Hybrid Essbase Cameron Lackpour Tim German Dan Pressman Developing Essbase Applications Like the best, most advanced Essbase conference there ever could be Advanced content
More informationCopyright 2014, Oracle and/or its affiliates. All rights reserved.
1 Oracle Database 12c Preview In-Memory Column Store (V12.1.0.2) Michael Künzner Principal Sales Consultant The following is intended to outline our general product direction. It is intended for information
More informationChapter 12: Indexing and Hashing
Chapter 12: Indexing and Hashing Basic Concepts Ordered Indices B+-Tree Index Files B-Tree Index Files Static Hashing Dynamic Hashing Comparison of Ordered Indexing and Hashing Index Definition in SQL
More informationUnit 3: Multiplication and Division Reference Guide pages x 7 = 392 factors: 56, 7 product 392
Lesson 1: Multiplying Integers and Decimals, part 1 factor: any two or more numbers multiplied to form a product 56 x 7 = 392 factors: 56, 7 product 392 Integers: all positive and negative whole numbers
More informationDeccansoft Software Services Microsoft Silver Learning Partner. SSAS Syllabus
Overview: Analysis Services enables you to analyze large quantities of data. With it, you can design, create, and manage multidimensional structures that contain detail and aggregated data from multiple
More informationSomething to think about. Problems. Purpose. Vocabulary. Query Evaluation Techniques for large DB. Part 1. Fact:
Query Evaluation Techniques for large DB Part 1 Fact: While data base management systems are standard tools in business data processing they are slowly being introduced to all the other emerging data base
More informationQlik Sense Enterprise architecture and scalability
White Paper Qlik Sense Enterprise architecture and scalability June, 2017 qlik.com Platform Qlik Sense is an analytics platform powered by an associative, in-memory analytics engine. Based on users selections,
More informationPerformance Tuning. Chapter 25
Chapter 25 Performance Tuning This chapter covers the following topics: Overview, 618 Identifying the Performance Bottleneck, 619 Optimizing the Target Database, 624 Optimizing the Source Database, 627
More informationIndexing. Week 14, Spring Edited by M. Naci Akkøk, , Contains slides from 8-9. April 2002 by Hector Garcia-Molina, Vera Goebel
Indexing Week 14, Spring 2005 Edited by M. Naci Akkøk, 5.3.2004, 3.3.2005 Contains slides from 8-9. April 2002 by Hector Garcia-Molina, Vera Goebel Overview Conventional indexes B-trees Hashing schemes
More informationOracle 12.2 Analytic Views:
Oracle 12.2 Analytic Views: Worth A Look Karen Cannell kcannell@thtechnology.com TH Technology About Me Mechanical/SW Engineer - Analyzed, designed, developed, converted, upgraded, enhanced legacy & database
More informationCalculations that Span Dimensions
Tip or Technique Calculations that Span Dimensions Product(s): Report Studio, Crosstabs, Dimensional Expressions Area of Interest: Reporting Calculations that Span Dimensions 2 Copyright Copyright 2008
More informationFILE SYSTEMS, PART 2. CS124 Operating Systems Fall , Lecture 24
FILE SYSTEMS, PART 2 CS124 Operating Systems Fall 2017-2018, Lecture 24 2 Last Time: File Systems Introduced the concept of file systems Explored several ways of managing the contents of files Contiguous
More informationPARALLEL & DISTRIBUTED DATABASES CS561-SPRING 2012 WPI, MOHAMED ELTABAKH
PARALLEL & DISTRIBUTED DATABASES CS561-SPRING 2012 WPI, MOHAMED ELTABAKH 1 INTRODUCTION In centralized database: Data is located in one place (one server) All DBMS functionalities are done by that server
More informationOverview. DW Performance Optimization. Aggregates. Aggregate Use Example
Overview DW Performance Optimization Choosing aggregates Maintaining views Bitmapped indices Other optimization issues Original slides were written by Torben Bach Pedersen Aalborg University 07 - DWML
More informationCXO-Cockpit Hands-On MDX Workshop. Marcel Legerstee Jeroen de Soeten
CXO-Cockpit Hands-On MDX Workshop Marcel Legerstee Jeroen de Soeten Agenda o MDX Query basics: structure & concepts o CXO-Cockpit Cube Calculations o Basic calculations o Using MDX functions o CXO-Cockpit
More informationProcessing of Very Large Data
Processing of Very Large Data Krzysztof Dembczyński Intelligent Decision Support Systems Laboratory (IDSS) Poznań University of Technology, Poland Software Development Technologies Master studies, first
More informationDrillbridge: Easy Hyperion Drill-through with No Redevelopment
A Saxifrage Systems LLC White Paper by Jason Jones February 2015 Drillbridge: Easy Hyperion Drill-through with No Redevelopment Table of Contents Summary... 3 The Problem... 3 The Solution... 3 The Design
More informationMDX 101 Getting Multi-Dimensional
MDX 101 Getting Multi-Dimensional Alex Whittles Alex@PurpleFrogSystems.com PurpleFrogSystems.com PurpleFrogSystems.com/blog @PurpleFrogSys Slides: http://bit.ly/mdx101 Thank you to our AWESOME sponsors!
More informationAdvanced Data Management Technologies
ADMT 2017/18 Unit 13 J. Gamper 1/42 Advanced Data Management Technologies Unit 13 DW Pre-aggregation and View Maintenance J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Acknowledgements:
More informationSAP Crystal Reports and SAP HANA: Options and Opportunities (0301)
September 9 11, 2013 Anaheim, California SAP Crystal Reports and SAP HANA: Options and Opportunities (0301) Jaclyn Churcher Learning Points Connectivity options to SAP HANA for SAP Crystal Reports Two
More informationExporting Data in PBCS With Business Rules
Exporting Data in PBCS With Business Rules Introduction If your environment is a cloud product, whether it be PBCS or epbcs, one thing that is critical to understand is the backups produced in the Migration
More informationOLAP Introduction and Overview
1 CHAPTER 1 OLAP Introduction and Overview What Is OLAP? 1 Data Storage and Access 1 Benefits of OLAP 2 What Is a Cube? 2 Understanding the Cube Structure 3 What Is SAS OLAP Server? 3 About Cube Metadata
More informationAdvanced Data Management Technologies
ADMT 2017/18 Unit 10 J. Gamper 1/37 Advanced Data Management Technologies Unit 10 SQL GROUP BY Extensions J. Gamper Free University of Bozen-Bolzano Faculty of Computer Science IDSE Acknowledgements: I
More informationCS6303 Computer Architecture Regulation 2013 BE-Computer Science and Engineering III semester 2 MARKS
CS6303 Computer Architecture Regulation 2013 BE-Computer Science and Engineering III semester 2 MARKS UNIT-I OVERVIEW & INSTRUCTIONS 1. What are the eight great ideas in computer architecture? The eight
More informationAdvanced Multidimensional Reporting
Guideline Advanced Multidimensional Reporting Product(s): IBM Cognos 8 Report Studio Area of Interest: Report Design Advanced Multidimensional Reporting 2 Copyright Copyright 2008 Cognos ULC (formerly
More information