Customer Cubing Services Education Session
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Transcript of Customer Cubing Services Education Session
© 2010 IBM Corporation
Business Analytics
Cubing Services
Cubing Services Development
29 April 2010
© 2010 IBM Corporation2 Footer Field
Business Analytics
Disclaimer
The information on any new products in this presentation is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information on new products is for informational purposes only and may not be incorporated into any contract. The information on new products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. The development, release, and timing of any features or functionality described for our products remains at our sole discretion.
© 2010 IBM Corporation3
Business Analytics
Agenda
IBM OLAP portfolio – how Cubing Services fits in
Cubing Services architecture and clients
Lifecycle
Best Practices
New features in 9.7 – dimensional security, virtual cubes
Performance and scalability proof points
© 2010 IBM Corporation4
Business Analytics
OLAP portfolio from IBM – complete coverage
OLA
P
Read/write user communities 64 bit in-memory fast performance Larger highly dynamic data sets -------------------------------------------- Dynamic changing dimensions and hierarchy,
what-if scenarios, data contribution Common dimensions shared across multi-
cube models Budgeting and planning Personal and corporate data sources
Large read only user communities Moderate ‘focused area’ data sets Pre-aggregated static fast results -------------------------------------------- Off-line portable/ partitioned storage Rapid startup for advanced business
user self service modeling Automatic time series analysis &
trending Point in time data Personal and corporate data sources
Large user communities Large data sets Planned performance/optimization facilities -------------------------------------------- Optimized ROLAP Zero Latency High volume concurrency Largest Data Volumes Centralized IT management of information
IBM Cognos PowerCube
Optimized for broad, general purpose BI usage
IBM Cognos TM1
Optimized for write back and high volatility applications
IBM Infosphere Warehouse Cubing Services
Optimized for very large datasets with very large dimensions
Specialized OLAP Modeling Tools
Turbo IntegratorMO
DEL
ING
Transformer Design Studio
USE
R
Complete, Consistent View of Information
IBM Cognos BI / FPM
© 2010 IBM Corporation5
Business Analytics
InfoSphere Warehouse Cubing Services
InfoSphereWarehouse
Warehouse Modeling
Cubing Services
Cognos 8Excel & 3rd
party toolsIBM tools
Large enterprise IT deploymentsPrimary OLAP Use:
Ideal for:• Very large data sets with very
large dimensions – SKU level data
• Enterprise rollouts requiring near real time data
Primary owner - IT departments
Analytics & reporting forbanking, retail & insurance
Because of its unique ability to:• Accelerate OLAP queries
optimized on InfoSphere Warehouse
• Integrated IT tooling
© 2010 IBM Corporation6
Business Analytics
Key capabilities - InfoSphere Warehouse Cubing Services
Lower cost of ownership bringing design, query and
performance together
Integrated IT tooling
Support many users with accelerated OLAP
queries
Integrated administration for warehouse and cubes
Unlimited scalability
Supporting a large number of deep dimensions with
SKU level data64-bit high performance caches for very large data
volumes and queries
Near real time data access with high speed
performance
High performance ROLAP solution
Cognos 8 front end clients and Microsoft Excel via Pivot table servicesIndustry standard ODBO & XMLA APIs enable 3rd party applications
High performance, scalable cubing engine
Integrated design tooling with Design Studio &
Rational Data Architect
SQL access optimized for InfoSphere Warehouse
The Optimization Advisor accelerates all SQL-based queries in the
warehouse
Increase performance & scalability using
optimization features of InfoSphere Warehouse
© 2010 IBM Corporation7
Business Analytics
InfoSphere Cubing Services architecture
CubingServices
InfoSphereWarehouse
MQT’s, MDC’s
Excel
AlphabloxMDXSQL
Web Administration Console
Eclipse Design StudioOLAP Metadata Optimization
AdvisorMDX
Calculation Builder
Cube Manager
Server Management
64-bit in-memory cachingNo data duplication
OD
BONative API
XMLA
BI ToolsCube Security Manager
Cognos
© 2010 IBM Corporation8
Business Analytics
Excel 2003, 2007
• Pervasive BI tool
• Real-time, live connection to warehouse data via Excel
• Can save point-in-time data, and refresh at any time
• Full use of pivot table services for analysis
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Cognos 8.4 SP2 and later
© 2010 IBM Corporation10
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Cubing Service Lifecycle
© 2010 IBM Corporation11
Business Analytics
Run
Design
Optimize
Deploy
AnalyzeCube Server
Administration Console
Design Studio
Cubing Services Advisor
DB2 Design Advisor
Cubing Services Lifecycle – Stages 1 through 5
© 2010 IBM Corporation12
Business Analytics
Lifecycle Stage 1 – DesignCreating a Cube
Design
• Goal: Define multidimensional metadata that describes the information that is stored in relational tables
Cube Model and Cube Measures Dimensions Hierarchies Levels Members
Star Schema• fact table• dimension tables• outboard tables
© 2010 IBM Corporation13
Business Analytics
Lifecycle Stage 1 - DesignDesign Studio
DB2 LUW
OLAP Metadata
DB2
BI Designer
Export Import
Deploy for runtime
CreateEditDropAnalyze
Reverse engineerschema
Metadata files
© 2010 IBM Corporation14
Business Analytics
© 2010 IBM Corporation15
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Lifecycle Stage 2 – Optimize
Cube server can generate one or more SQL queries to calculate result set for each MDX query
Queries that aggregate large volumes of data can be time consuming
MQT = Materialized Query Table; calculate aggregations up front
DB2 Optimizer can access MQT instead of performing the aggregations over detail data when the query is processed
MQTs are defined like views, physical like tables and transparent to applications like indexes
Optimize
MDX
SQL Data
Result set
MQT
Cube Server
DB2
© 2010 IBM Corporation16
Business Analytics
Lifecycle Stage 2 – Optimize Cube Optimization: Overview
DB2
MQTs
OLAP Metadata
Optimization Advisor
Model-driven MQT generation
BI Designer
Advise Advise Admin Console
DBA
Design Studio
© 2010 IBM Corporation17
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Lifecycle Stage 2 – Optimize Cube Optimization: Overview (2)Model driven advisor recommends MQTs for cubes in a cube model
Recommendation is based on metadata, database statistics and data sampling.
Time and resource constraints can be applied to recommendations.
Advisor recommends MQTs that are optimized for Cubing Services.
The advisor produces SQL scripts that create and refresh MQTs
Integrate refresh script in SQW process flows that update the data warehouse tables.
© 2010 IBM Corporation18
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Lifecycle Stage 3 - Deployment Admin Console
Deploy
DB2OLAP Metadata
MQTs
Cube Server
Admin Console
OLAP metadata
Import
Metadata files
Admin Console
DBA
© 2010 IBM Corporation19
Business Analytics
Lifecycle Stage 3 - Deployment
A cube is accessible by OLAP clients if it is running on a cube server. Use the Cubing Services administration features in Administration Console to:
– Import OLAP metadata (that was previously exported via Design Studio) into the metadata database for test/production deployments
– Map metadata to relational data source
– Create and configure cube server
– Deploy cubes to cube server
– Configure cube runtime tuning parameters
– Define cube security
– Control cube server and cube runtime states (start, stop, …)
© 2010 IBM Corporation20
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© 2010 IBM Corporation21
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Lifecycle Stage 4 - Runtime
Cube server and
cube administrationCube Server
Alphablox Excel
DB2
MQTs
OLAP Metadata
Optimization Advisor
MDX
Admin Console
Other BI tools
Run
© 2010 IBM Corporation22
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Lifecycle Stage 4 - Runtime Starting a cube
Cube Server
DB2
Dim MemberCache
SQL
Data cache
SQL (seed query)
© 2010 IBM Corporation23
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Cube Server
DB2
MDX calculation engine
Data cache
SQL
Dim MemberCacheResult set
MDX query
Lifecycle Stage 4 - Runtime Query Processing
• Simple, multi-pass SQL
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Performance Factors
Cubing Services is fundamentally a ROLAP-style solution– Depends on underlying database performance for some aspects of its performance
characteristics– Leverages MQTs, MDC, and other relational database optimizations– Includes additional caching layers for performance
© 2010 IBM Corporation25
Business Analytics
Tune configuration
Lifecycle Stage 5 - Analyze/Optimize
DB2
DB2DesignAdvisor
Cube Server
Log filesDBA
Create MQTs, MDC,
Indexes
Analyze
DB2W Admin Console
© 2010 IBM Corporation26
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Best Practices
© 2010 IBM Corporation27
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Cube Design: Best Practices for Facts
Star schema design
–Good performance starts with good relational modeling
–Define referential integrity (even if they are only informational constraints)
Schemas with multiple fact tables
–Create a view joining the fact tables and include the view as the facts object in the cube
Express measure calculations in SQL in the measure definition in a cube
–This allows Cubing Services to push down measure calculations to DB2 and exploit all of the optimizations provided by DB2
Design
© 2010 IBM Corporation28
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Cube Design: Best Practices for Dimensions
Avoid large, flat dimensions
– Explore the creation of artificial levels in the hierarchy if it is too ‘flat’ (for example if dimension members have thousands of children)
When specifying level keys:
– Use single column level keys (or as few columns as necessary)
– Use integer values for level keys
Design
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Cube Design: Best Practices for Dimensions
Number of dimensions to define in a cube– No explicit limit, but a large number of dimensions (more than 12) can have the following
negative performance impacts: • Increased memory usage - Memory storage cost of tuples increases proportionately
since a tuple consists of a member from each dimension• SQL query processing time - SQL generated by Cube Server will be longer and more
complex and have a lower probability that DB2 will use an MQT to respond to the SQL query
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Number of members you can have in a dimension or cube– Theoretical limit is the maximum integer value supported by the JVM– In reality, there is a trade off between two approaches:
• Single large cube• - Reduced memory footprint• --- All queries share a single instance of the cube members• - Cache management• --- Caching a single large instance may not be practicable• Multiple smaller cubes (preferable)• - Caching and manageability is easier• - Does require an increase in memory footprint• --- Cube members are duplicated
Cube Design: Best Practices for Dimensions
Design
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Deployment: Best Practices
Cube Server configuration
Uses two consecutive ports (firewall)
Limit number of cubes based on query load, cube sizeCube configuration
Adjust cache strategy and size as necessary
Enable/disable automatic member cache refreshSecurity configuration
Role based; enabled by default
Configurable for each cube; restricts query access
Authentication performed by DB2
Authorization determined by cube server
Deploy
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Runtime: Best Practices – Memory UsageRun
Factors that affect memory usage
– Number of running cubes and their cache configuration
• Member cache (static default, dynamic)
– Memory usage ~ 1.1KB per member• Data cache
– Memory usage ~ 1KB per member– Allocate 10 % of metadata cache size
– Query processing
• MDX query workload (e.g. tuple count)
• Number of concurrently processed queries Keep total cube server memory footprint below 10 GB to avoid memory management overhead of Java.
Run very large cubes on separate cube servers for better scalability.
Run cubes that are accessed by many users concurrently on multiple cube servers.
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Runtime: Best Practices – Query Performance Factors that affect query performance
MDX query type and complexity; ‘costly’ measure definitions
Avoid ‘unnecessary’ measures that result in expensive database calculations (e.g. count(distinct), sum(distinct))
Non-Empty processing requires two-passes; use only with sparse data sets
Result set size
Intermediate result set
Final result set
Number of concurrent users
Available system resources (e.g. heap memory)
Data cache state (cold vs. warm)
Cold cache performance depends on database performance – database
tuning (such as MQTs, indexes, partitioning) is crucial
Define seed query to pre-populate cache upon cube startup
Run
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Analyze and Optimize: Best Practices
Cube server creates six log files (activity, exception, trace, MDX, performance, SQL)
Enable MDX log, SQL log and performance log to analyze query performance
MDX log captures processed queries
SQL log captures generated queries
Performance log captures cache hits and misses, the MDX query elapsed time and the SQL query elapsed time
Higher cache misses indicate too small cache size relative to the amount of data being fetched by the queries. Consider increasing the data cache size and use of a seed query.
High SQL elapsed times could indicate that appropriate indexes and/or MQTs have not been created or are not being used effectively by the DB2 optimizer.
Use SQL queries as an input workload to the DB2 Design Advisor. The tool will recommend additional MQTs, indexes, MDC, and other optimization objects to further optimize DB2 for this Cubing Services workload.
Analyze
© 2010 IBM Corporation35
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New in Cubing Services 9.7Faster performance, richer functionality, easier to use
Faster performance
Virtual cubes– Enables fast performance in a low latency, real-time warehouse– Facilitates better data management
Dimensional security– Fine grain access control down to the dimension members
Remote server management
© 2010 IBM Corporation36 Footer Field
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Faster cube load time than 9.5
• Cubes load on average nearly twice as fast in 9.7
• Uses less memory during cube load
© 2010 IBM Corporation37 Footer Field
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Query response times improve
Results:
• Cold cache response times on average 9.7.1 is 3.5 times faster than 9.5.2
• Warm cache shows similar improvement
• Disk backed data cache leaves more memory for query execution.
Benefits:
• Faster cold cache response times makes real-time OLAP easier since cache refreshes are less impactful to user experience 9.5.1 9.5.2 9.7 9.7.1
© 2010 IBM Corporation38 Footer Field
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Combines cubes with nearly identical dimensions
Virtual cube used as source for another virtual cube
Combines cubes with common Time dimension
Virtual cubes combine two
cubes
Virtual cubes overview
Sales
Store SalesWeb Sales
Inventory Sales
Inventory
© 2010 IBM Corporation39 Footer Field
Business Analytics
• A virtual cube is a logical cube that is defined in terms of exactly two existing cubes – “real” or “virtual”• Cubes can be merged in pairs to any depth
• Virtual cubes combine two cubes that:• Share a dimension which is the basis for merging them to
form a virtual cube
• Can belong to different cube models
• Reside in the same database
• Are assigned to the same cube server
Definition
© 2010 IBM Corporation40 Footer Field
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Primary usage scenarios
• Improve latency by creating historic and delta cubes
• Currency transformations by using currency lookup cube
• Combine data with unlike dimensions or data split by region
© 2010 IBM Corporation41 Footer Field
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Delta cubes improve performance and lower latency
Time
AllSales
HistoricSales
Current Month Sales
Current Month
Original CubeAll Sales cube
• Small CurrentMonthSales cube is updated daily, hourly, or more frequently
• Large HistoricSales only updated once a month when roll in data from CurrentMonthSales
• Technical benefits:
• Faster MQT updates
• Cache remains warm for HistoricSales
© 2010 IBM Corporation42 Footer Field
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Performance improvement example
100 times faster* average query response time against fact data updated incrementally using delta cube
* Results will vary based on ratio of data between the small delta cube and the large historic cube.
© 2010 IBM Corporation43 Footer Field
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Currency conversion scenario
Store Sales Web SalesCurrencyConversion
Store Sales(multiple currencies)
Web Sales(multiple currencies)
Transform sales data so user can see in variety of currencies
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Combines cubes with nearly identical dimensions
Virtual cube used as source for another virtual cube
Combines cubes with common Time dimension
Combine data with unlike dimensions or data split by region
Sales
Store SalesWeb Sales
Inventory Sales
Inventory
© 2010 IBM Corporation45 Footer Field
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Merging cubes - Dimension Merging
Sales Cube Inv Cube Virtual Cube
Product Product Product
Time Time Time
Inventory Inventory
Store*
Merge dimensions with identical names
Unmatched dimensions added to virtual cube
User can hide unwanted dimensions
Users can merge dimensions with different names by changing the names to match. Details follow.
* The dimension is hidden for virtual cube
© 2010 IBM Corporation46 Footer Field
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Merging cubes - Level Merging
Sales East Sales West All SalesCountry Country CountryState Province State ProvinceCity City CityNeighborhood Neighborhood
Sales East Sales West All SalesCountry Country CountryRegion Province Region ProvinceState State
Mismatched levels produce meaningless
results
For common dimensions, levels are merged starting from top of hierarchy
Unmatched names are
concatenated
User can’t change level names
© 2010 IBM Corporation47 Footer Field
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Member Merging
All cube members will be in the virtual cubeCube A and Cube B have
shared dimension [Measures]
Cube A Cube B Virtual Cube
[Store Sales] [Store Sales] [Store Sales]
[Unit Sales East] [Unit Sales East]
[Unit Sales West] [Unit Sales West]
Merge members with identical
names
Unmatched members added to
virtual cube
Virtual Cube
CubeA CubeB
© 2010 IBM Corporation48 Footer Field
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Merge operators
• Cubes are merged based on operator
• +, -, /, *, Max, Min
• Examples:
• Merge using add operation "+“• select from [Virtual Cube] =
• select from [CubeA] + select from [CubeB]
• Merge using MAX operator• select from [Virtual Cube] =
• MAX(select from [CubeA], select from [CubeB])
Virtual Cube
CubeA CubeB
© 2010 IBM Corporation49 Footer Field
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Dimensional security with virtual cubes
• Dimensional security for a virtual cube is inherited from its cubes
• There are no specific security settings for virtual cube
• Users will have to set security on the base cubes
Virtual Cube
CubeA CubeB
Set dimensional security here Set dimensional
security here
Intersection of security settings from CubeA
and CubeB
© 2010 IBM Corporation50 Footer Field
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Dimensional security
Store 1
CA
USA
Without dimensional security
Store hierarchy
San Jose
Store 1
WA
Seattle
Store 2
With dimensional security
USA
San Jose
Store hierarchy
CA
Seattle
WA
Store 2
• Fine-grained access control to OLAP metadata• Allows you to limit the members of a dimension that a role can
access
• By default, allows access to all members
© 2010 IBM Corporation51 Footer Field
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Security model concepts
• ResourceAn OLAP object on which security is defined, e.g. cube, dimension/hierarchy (including Measures dimension)
• RoleA group of users that share the same security privileges
• PolicyA privilege or rule for accessing a resource, e.g. read access allowed to cube, read access denied to dimension/hierarchy members
• AuthorizationAn association among a resource, role, and policy
© 2010 IBM Corporation52 Footer Field
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Security model lifecycle1. Create security model in
Design Studio
2. Export security model from Design Studio to XML file
3. Import security model from XML file to Admin Console
4. View security model in Admin Console
5. Start cube server or refresh security
6. Modify security model (if needed)
7. Delete security model from repository
© 2010 IBM Corporation53 Footer Field
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Security enforcementSingle role/single policy
A user in a single role that is assigned a single policy on a dimension is allowed to access the set of allowed members except the set of denied members
Example:
Allowed Members = {Ascendants([Store].[CA]), Descendants([Store].[CA])}
Denied Members = [Store].[City].MembersUSA
CA
Store1
San Jose
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Security enforcementMultiple roles/multiple policies
A user in multiple roles or a single role with multiple policies is allowed to access union(allowed members) except union(denied members)
Union(denied members)Union(allowed members) DM
DM
AM DM
DM
DM
DMAM = Allowed Member
DM = Denied Member
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Security enforcementHierarchy navigation
Restricted members are skipped in operations like .parent and .children.
Member
Parent Member
Children
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Remote cube server management
Repositorydatabase
WebSphere
Server 3
Server 1
Cube Server 3
Server 2
Cube Server 2
Cube Server 1
Admin Console
© 2010 IBM Corporation57
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Proof pointsRecent success stories
Telecom customer
Retail customer
IBM integrated supply chain
© 2010 IBM Corporation58
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Telecom customer overview
Industry: Telecom
InfoSphere Balanced Warehouse like system– Database module - 42 data partitions– Application module - Model 9133-55A, 4 PowerPC_POWER5 processors, 32GB memory
Software– DB2 9.5– InfoSphere Warehouse 9.7.1– Cognos 8.4
Warehouse– Four star schemas– Largest fact table has 1.5 billion rows. Other fact tables have 50 million, 20 million and 8 million rows.– 8-12 dimensions per fact table (most shared)
Cubes– Cube per star schema– Virtual cube merges the four cubes
• 12 dimensions• 25 measures – varying complexity
Cognos reports took 5-7 seconds
© 2010 IBM Corporation59
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VirtualCube 2
Reactivation
PerformanceDashboard
TossAdjustment
Activation ARPU
VirtualCube 1
Telecom Customer - Cubes
• Assess whether monthly net additions are improving.
• Calculate the average revenue per user (ARPU) to see if it matches industry competitors. This is the total revenue divided by the total subscribers for a particular time period
• Determine if subscriber additions have increased from the same period the previous year.
• Analyze the churn rate for cell phone subscriptions over time to determine whether the company is gaining or losing ground compared to competitors
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Telecom Customer - Details
Tuning– New database with no optimization– Defined primary keys on dimension tables– Defined constraints (not enforced) on star schema joins– Ran Cubing Services Advisor (no data sampling)
• Recommended indexes on fact table foreign keys and dimension level keys• Recommended MQTs
– Built recommended indexes and MQTs
Additional tuning that could be done– Define MDC for fact table– Run Cubing Services Advisor with data sampling to get better recommendations– Run DB2 Design Advisor for additional index/MQT recommendations
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Retail customer overview
Industry: Retail
IBM Smart Analytics System 7600 v9.7 (pSeries and AIX)– Database module - 1 admin node + 2 data nodes (8+1 DPF partitions), Power6, 4-core, 32 GB– Application module - 4-core, 32 GB (actually 8-core 64 GB but only used half)
Software– DB2 9.7.1– InfoSphere Warehouse 9.7.1– Cognos 8.4
Warehouse– One star schema– Fact table has 2 billion rows– Four dimensions
Cubes– One cube
• 4 dimensions• 12 measures – simple aggregations
Cognos reports took 5-7 seconds
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AllTime
Time Category Store
Year
Month
Week
Organization
Category
Department
AllCategories
Region
Store
Area
AllLocations
Retail Customer - Model
Vendor
Vendor
AllVendors
• Which stores generated the highest total sales
• How did sales compare for particular products from the previous year
• Which region was the most profitable
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IBM integrated supply chain
Software:– DB2 9.5.2– InfoSphere Warehouse 9.5.2– Alphablox 9.5.2– AIX 5.3
Warehouse:– 1 large table with dimensions embedded within fact table – over 70 million rows and growing– Offline for maintenance only 1x week for 4 hours– Refreshing cubes takes minutes in most case, up to 20 minutes for largest cube
300 users worldwide
Saved $6 million in first year, and expect to save more than double that in the second year
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Technical resources
Information Center for 9.7 http://publib.boulder.ibm.com/infocenter/db2luw/v9r7/index.jsp
Cubing Services Tutorial http://publib.boulder.ibm.com/infocenter/db2luw/v9r7/topic/com.ibm.dwe.tut.cube.doc/tutcubetutorial.html
Virtual cubes tutorialhttp://publib.boulder.ibm.com/infocenter/db2luw/v9r7/topic/com.ibm.dwe.tut.cube.doc/tutcubetutorial.html
Dimensional security developerWorks articlehttp://www.ibm.com/developerworks/data/library/techarticle/dm-0909securityinfospherecubing/
Virtual cubes developerWorks article http://www.ibm.com/developerworks/data/library/techarticle/dm-0909infospherevirtualcubes/
© 2010 IBM Corporation65
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Backup
© 2010 IBM Corporation66
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IBM Cognos PowerCube™ – MOLAP
Business modeling
OLAPRelational Cognos ReportsFlat files
Access to All Data
PowerCube
Trending / Slice & DicePrimary OLAP Use:
Optimized for:• Highest consistent query
performance• Enterprise rollouts such as
internet delivery
Primary owner – Business Analyst or LOB IT
Operational reportingSales trend & ad hoc analysis
Because of its ability to provide:
• Pre-aggregated compressed data that can be disconnected
• Automatic time series analysis, trending, & point in time data
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Key capabilities - Cognos Transformer & PowerCube™
Flexible, fit for purpose, pre-aggregated storage for optimal performance
Partition information by time, level, or security
Build, design, query in disconnected situations*
Incrementally add data hourly, daily, etc
High performance
portable storage
Simple to complex modeling addressing departmental needs
Multiple drill paths, multiple time dimensions
Business rules, calculated members or levels,
custom time functions
Flexible for unique business
needs
With no SQL skills:Merge data from SAP, flat files, other OLAP
dimensions, & Cognos 8
Time series analysis, point in time measures
Enable the businessto model data for their
business problems
Self service business modeling
Series 7 PowerPlay or Cognos 8 front end clients
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What-if scenario modelingPrimary OLAP Use:
Ideal for:• write-back planning
applications in moderate sized communities;
• complex models demanding read/write interactivity
Primary owner – LOB IT
Operational PlanningFinancial analytics &
reporting
IBM Cognos TM1 – in memory MOLAP
OLAPRelational Flat files
Access to All Data
TM1 Servers
TM1 Architect
TM1 clients Cognos 8 clients
Because of its unique:• on demand aggregation and
calculations with 64 bit in- memory processing
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Key capabilities - Cognos TM1
Immediate answers to complex questions on
highly volatile data
Business driven analysis for real time response
Create simulations easily to interactively test
business assumptions
Advanced rules language for complex financial
applications
Enterprise level collaboration
Quickly load & merge large data volumes (intra-day or nightly) from ODBC
/ ODBO & flat files
Data import & scripting engine in a multi-cube
architecture with shared dimensions
Compute hierarchical aggregations & complex calculations on demand
64 bit in-memory processing
Cognos TM1 or Cognos 8 front end clients
Enterprise data contributions and what-if
scenario planning
Easily create and access reports or input templates
deployed via Excel or web
On the fly dimensional hierarchy changes & self service ad-hoc analysis
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Client perspective
Results without dimensional security
Results with dimensional security
The value for Store Sales is not the same for All Store, USA, and CA,
i.e. no visual totals.
Query: select [Measures].[Store Sales] on columns, [Store].members on rows from [sales]
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Working with Virtual Cubes
Users will define virtual cubes using Design Studio.
Administrative tasks for Virtual cubes are performed using the Cubing Services Administration Console
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Create a virtual cube using Design Studio
Cubes
Virtual Cube
Virtual Calculated measures
Virtual Measures
Only renamed virtual dimension will
appear here, not all virtual dimensions
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Create a virtual cube in Design Studio Add virtual cube
Optionally rename dimensions to cause merging of desired ones– Users can hide dimensions so they don’t exist on the virtual cube– Optionally specify a default member for the dimension– Optionally rename members to cause merging them
• Members can have their own merge operators• Members can also be hidden on the virtual cube
– Optionally define calculated members
Export the model
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Use dimension browser in Design Studio to ease design
New Design Studio usability enhancement
– Display hierarchy members directly from the Design Studio
– Support all member types: MDX and SQL calculated members
– Support all hierarchy types
– Support all real and virtual cubes
Use to shorten and simplify design time of real and virtual cubes
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Managing virtual cubes from Admin Console
In Admin Console, virtual cubes belong to a cube model called VIRTUAL. This is where they can be deleted.
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Managing virtual cubes from Admin Console
Starting, deploying to a cube server, stopping, and undeploying virtual cubes operations are similar to real cubes except for a few new dependencies:
– A virtual cube can be started/deployed only after both its direct cubes are started/deployed
– A cube or virtual cube can only be stopped/undeployed after all the virtual cubes that directly use it are stopped/undeployed
Virtual Cube
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Create security model in Design Studio - Cube security
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Create security model in Design StudioDimension security (non-Measures dimension)
Allowed Members and Denied Members must be MDX expressions that evaluate to a set of members
MDX is validated for syntax errors only
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Create security model in Design StudioDimension security (non-Measures dimension) (cont’d)
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Create security model in Design StudioDimension security (Measures dimension)
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Create security model in Design Studio - Roles
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Export security model from Design Studio to XML file
Only security-enabled objects are exported
Only policies and roles that are referenced by an authorization are exported
XML file containing the security model is different from XML file containing the cube model
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Business Analytics
Import security model from XML file to Administration Console
1. Select the database that contains the tables that are referenced by the cubes in the XML file
2. Specify the XML file that contains the security metadata
3. Map roles
4. Select collision resolution option - Merge Replace, Merge Ignore, Replace
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Business Analytics
Import security model from XML file to Administration Console - Collision resolution options
Old security model
Dimension: Store
Policies: StorePolicy1
Authorizations: (Role1,StorePolicy1), (Role2,StorePolicy1)
Dimension: Product
Policies: ProductPolicy
Authorizations: (Role3,ProductPolicy)
New security model
Dimension: Store
Policies: StorePolicy2, StorePolicy3
Authorizations: (Role1,StorePolicy2), (Role3,StorePolicy3)
Dimension: Measures
Policies: MeasuresPolicy
Authorizations: (Role3,MeasuresPolicy)
Collisions are detected and resolved at the resource level
Example: Merge Replace
Merged security model
Dimension: Store
Policies: StorePolicy2, StorePolicy3
Authorizations: (Role1,StorePolicy2), (Role3,StorePolicy3)
Dimension: Product
Policies: ProductPolicy
Authorizations: (Role3,ProductPolicy)
Dimension: Measures
Policies: MeasuresPolicy
Authorizations: (Role3,MeasuresPolicy)
Added
Kept
Replaced
Accepted
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Business Analytics
View security model in Administration Console
View the security metadata associated with a cube and its dimensions by selecting Manage Cube Models <cube model> Edit <cube> View Privileges
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Business Analytics
View security model in Administration Console (cont’d)
View the security metadata associated with a role by selecting Manage Roles <role> View Privileges
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Business Analytics
Start cube server or refresh security
Hit “Reload Security” button to update the security metadata in a running cube server
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Business Analytics
Delete security modelTo remove the security metadata associated with a cube and its dimensions, select Manage Cube Models <cube model> Edit <cube> Delete Cubes and Security
Delete Cubes and Security
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Business Analytics
Miscellaneous Design Studio wizards and explorers to manage and view security model
Import wizardImports security model from XML file to database in project or database explorer
Deploy wizardExports security model from database in project explorer to database in database explorer
Create database from reverse engineeringExports security model from database in database explorer to database in project explorer
Database explorer Displays security in the repository database including default cube security
© 2010 IBM Corporation90
Business Analytics
Thank you!