© 2012 IBM Corporation

IBM Cognos OLAP Cubes Re-Visited Juha Teljo

Business Intelligence Solutions Executive

What is IBM’s position on our cube technologies?

Strategic in-memory technologies

§  Dynamic Cubes: High-performance analytics over terabytes of data

§  TM1: Write-back application focused with robust business modeling and rules engine

Invest for on-going customer success

§  PowerCube: Portable, optimized for multi-dimensional analysis

§  Dimensionally Modeled Relational (DMR): Dimensional view over any relational database

IBM’s strategy is the right fit for the right business problem

New in IBM Cognos 10.2 – Dynamic Cubes

Modern  and  Legacy  Sources  

Applica4on  Sources  

3rd    Party  OLAP  Sources  

Rela4onal  Sources  

Dynamic Query Mode

Common  Business  Model  

Classic Query Mode

 Scorecards    Dashboards  

 Reports  

Ad-­‐hoc    Query  

Analysis    &  Explora4on  

Trend  &    Sta4s4cal  Analysis  

What-­‐If  Analysis  

PowerCubes

Open  Data  Access  

OLAP Over Relational

Dimensionally Modeled Relational

Large  Enterprise  Data  Warehouse  

Database  Aggregates  

Dynamic Cubes

§  High performance on high volume star or snowflake schemas in relational sources

§  Powerful in-memory

OLAP cubes §  Aggregate aware §  Easily-optimized

aggregates §  Shareable where

security is shared §  Accessible by all IBM

Cognos Interfaces §  Included with Cognos

Business Intelligence (no additional cost)

TM1

1.  Model  &  publish  

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2.  Deploy  &  manage  3.  Repor:ng  &  analy:cs  

4.  Op:mize  

Dynamic  Cube  Server  

Dynamic Cube Logs  

CM

Warehouse  

Dynamic Cubes Lifecycle

How does PureData Systems for Analytics (Netezza) fit in?

§  Now known as “PureData Systems for Analytics, Powered by Netezza Technology”

§  One of the PureData family of servers – this is the one with Netezza technology and is specifically tuned for BA/AA

§  Netezza is essentially a data warehouse and analytics appliance.

§  It has an MPP (massively parallel processing) database, not columnar as many presume, and specially designed hardware to make analytic DB queries very fast.

§  It can handle high volume, high access speed data – up to 10 petabytes

§  Viewed as simply another Database server to Cognos solutions

§  Not specifically designed for OLAP, but can be leveraged very effectively, especially with Dynamic Cubes, using aggregate tables

§  Should also be excellent for standardized BA reporting on large data volumes and help with DMR analysis

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System for Analytics

Client Success with Dynamic Cubes Enabling self-service BI at University of Colorado

Enable departments to self-service their own analysis needs

Dramatically improve analytics performance for business users

Optimize resources by saving IT time and effort with easy performance tuning

The need: Faced performance challenges optimizing their relational managed reports centrally delivered to all departments across the 4 campuses of University of Colorado. Report performance was not fast enough, and lacked the ability to enable individual departments to do their own analysis. The solution: In a little over two weeks, delivered a Dynamic Cubes solution that dramatically improved performance of existing reports and also enabled University of Colorado to make a cube available for self-service adhoc analysis for the first time. Real business results: •  Department reports that took over a day and required manual manipulation of results, now run in less than 3 seconds without any extra work •  Able to make a cube available for ad-hoc analysis, enabling self-service BI for departments •  Create dashboards that weren’t previously feasible due to performance challenges – now they run in 5 seconds or less!

“Dynamic Cubes helps us turn Cognos from a packaged reporting engine into a self-service BI engine”

“With Dynamic Cubes, performance will continue to be fast even as our data volumes grow”

— Molly Doyle Assistant Director for IRM

University Information Systems University of Colorado, Office of the President

University of Colorado

What about Size / Performance / Scale / Hardware …?

§ Some of the most common questions but most difficult to answer

§ The right answer changes depending on (to name a few):

– Data volumes – Underlying source data structure – Type of data & complexity (calculations, measures, etc) – Filters & security

§ IBM does not provide benchmark numbers

§ Red books and reference materials do provide general guidelines

§ Deep Dive tech jams are under consideration for Q2/Q3

§ For in-depth discussions look to your TSA or BASA for help

Cognos Dynamic Cubes Redbook

Application Objective Data Structure

Optimal Technology

Notes / Considerations

•  Write-back •  What-if analysis •  High-volatility apps

TM1 Medium data volumes Aggregates on the fly

•  High performance analytics •  Large data volume •  Star / Snowflake schema

Dynamic Cubes

Optimized aggregates Aggregate-aware

•  Operational / transactional system

•  Consistent performance PowerCubes

Low / medium data volumes Data movement into cube: Latency

Cube groups to manage volume

•  Operational / transactional system

•  Tightly control latency (cached & non-cached data)

•  Tight control over security

DMR (via DQM)

Low / medium data volumes Leverages Framework Manager model

No database aggregate support

Cube Technology Selection – Simple Decision Tree

What about Multiple OLAP technologies?

§ Is it a problem if a customer has more than one cube technology? –  NO

§ One Size does not fit all. It is ok to have multiple technologies

§ There is integrated functionality in the platform – Seamless Drill Through – Irrelevant to the end-user what the underlying technology is

§ Professional Report Authoring allow us to mix multiple cube technologies in same report

– Hides data source – Allows end-user to focus on data and results

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Customers licensing multiple cube technologies

§ PowerCubes, DMR, and Dynamic Cubes come as part of Enterprise BI in Cognos10.2

§ Cognos Insight included for Advanced Business Author upwards

§ TM1 (who need write-back) need to license IBM Cognos Analytics Server

§ IBM Cognos Analytics Server combined with Advanced Business Author upwards allows write-back to TM1 with Cognos Insight

§ For deeper integration - need to license TM1 Contributor in order to include the contributor widgets in Cognos Workspace.

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How does Dynamic Cubes affect Licensing - PVU

§ Use the sizing papers (Google Dynamic Cube Sizing) and CTP / TSA guidance to determine hardware requirements

§ If PVU entitlements are fully allocated – adding Dynamic Cubes will require more PVU

§ Dynamic Cube capacity counts against PVU capacity just like any other HW Capacity

§ If their machine is powerful enough, they won’t incur additional cost to include Dynamic Cubes

§ If they need a larger configuration, they will need additional PVUs

§ Reference the Pricing and Licensing Center for PVU information

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Variety  

Volume  Velocity  

Veracity  

•  Cognos  Business  Intelligence  10.2  •  Dynamic  cubes  •  BigInsights  •  Netezza  •  GreenPlum    •  Paraccel  •  AsterData  •  Vectorwise  •  IBM  DB2  Warehouse  

•  SPSS  Predic:ve  Analy:cs  for  data  mining  and  text  analy:cs  

•  InfoSphere  Streams  •  SPSS  models  

•  Real-­‐:me  scoring    •  Cognos  Real-­‐:me  Monitoring  

•  Cognos  Consumer  Insights  •  SPSS  Data  Collec:on      Plus  the  IBM  por/olio  IBM  Content  Analy6cs  (Filenet)  and  Customer  Experience  Suite  

•  BA  Risk  PorRolio  including  recent  acquisi:on  Varicent  

Enhanced  for  MORE  Volume   New  Analy4cal  Models  

Purpose-­‐built  Analy4cs  

Area  of  Investment  

Today’s breadth of Big Analytics from IBM Business Analytics