COGNOS DYNAMIC CUBES SET TO RETIRE TRANSFORMER …€¦ · •A ROLAP In-Memory engine that sits on...

10.2.2 Update: Pros & Cons

COGNOS DYNAMIC CUBES: SET TO RETIRE TRANSFORMER?

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Copyright 2015 Senturus, Inc. All Rights Reserved 2

Presentation Slide Deck on www.senturus.com

3Copyright 2015 Senturus, Inc. All Rights Reserved

• Presenters Introduction

• Senturus Overview

• Transformer & Dynamic Cubes Deep Dive

• How Do I Know If I am a Good Candidate for Dynamic Cubes?

• Customer Use Case

• Demo

• Special Offers

• Additional Resources

• Q & A

Today’s Agenda


Pedro Ining

Senior BI Architect

Senturus, Inc.

Introduction: Today’s Presenters


Jim Frazier

Vice President of Client Solutions

Senturus, Inc.

Who we are

SENTURUS INTRODUCTION

Technology Depth + Business Acumen

Senturus: Business Architects for Business Analytics


C-Level Business Acumen

Technical/Tool Expertise

Deep Data Experience

Project Management

Rigor

BusinessIntelligence

EnterprisePlanning

PredictiveAnalytics

900+ Clients, 1700+ Projects, 15 Years


Have you deployed or are thinking about deploying Cognos Dynamic Cubes as a replacement for a Transformer implementation?

• Yes, already deployed

• Yes, in process of deployment

• Yes, plan to deploy within the year

• Yes, plan to deploy after 1 year

• No

Poll Question


Dynamic Cubes & Transformer Deep Dive

IBM COGNOS DYNAMIC CUBES:SET TO RETIRE TRANSFORMER?

• What are Cognos Dynamic Cubes?

• Is Transformer going away?

• Should I replace Transformer with Dynamic Cubes?

• What’s the effort to replace Transformer with Dynamic Cubes?

• Will Dynamic Cubes resolve my current Transformer issues?

• Can I still use PowerPlay Studio against Dynamic Cubes?

Typical Customer Questions

11

Current Transformer Architecture

12Copyright 2015 Senturus, Inc. All Rights Reserved.

DataMart

or

Operational Cognos

FM

Ad-hoc

Reports

Cognos

Dashboards

Standard

Reports

Cognos

Transformer

Various cubes at different grains and

sizes

Excel

Or Text

Files

Cognos Transformer Pros


• Fairly easy to model, build, and deploy a cube

• ‘ETL’ like functionality, allows creation of cubes from a variety of data sources• Star Schema

• Excel and Text Files

• Operational Sources

• Performance• For properly sized cubes, performance is quite good

• Once the cube is build, very little on-going tuning is required

Cognos Transformer Pain Points


• Transformer build times are too long

• Size limitations prevent full analysis of data• Create separate cubes for different years

• Transformer is a 32bit app and consequently limits the file size to 2GB

• Various partitioning schemes are required to implement cubes with sizes > 2GB

• Performance• Large cubes slow as you nest dimensions

• Suppress ZERO is expensive for large data sets

• Drill through to detail • Requires different packages and harder to configure

Cognos BI Stack

• A ROLAP In-Memory engine that sits on top of star-schema data warehouse

• Does not extract all data to build a physical cube

• Cube startup is relatively quick

• Uses a variety of in-memory and disk caches to enable fast query retrieval

• Not limited by the physical limitations of cube size like Transformer

• Can query the full breadth of data warehouse facts through the use of database and in-memory aggregates

• Aggregate aware query engine

• Requires optimization maintenance processes in order for the cube to continually perform adequately

Dynamic Cubes: Key Architectural Differences

16

Dynamic Cubes Architecture

Dynamic Cubes Aggregate Layers

18

Load time of In-Memory aggregates will depend on performance of the in-database aggregates layer

Dynamic Cubes Development Lifecycle

19

Key part of

Lifecycle

Dynamic Cubes Product Evolution

20

Dynamic Cubes was initially released in 10.2 and IBM has continually added features that may close the features gap between Dynamic Cubes and Cognos Transformer

Dynamic Cubes Product Evolution

21

Dynamic Cubes Support of Transformer Features


Relative Time Support

• Supported

• Custom Relative time became available in 10.2.1 FP3

• Custom Single Period e.g. Same Month, Last Qtr

• Custom Period-to-date e.g. Qtr to Date, Last Year

• Custom N-period running total e.g. Trailing Six Months, Next Year

Semi-Aggregate Time-State Rollups

• FIRST, LAST are supported but cannot be optimized in-memory

Transformer Style Security – Suppress, Apex, Cloak

• Can be replicated via MDX Expressions within dimension security

Orphan Categories

• Not supported as this should be handled in the star schema

HOW DO I KNOW IF I AM A GOOD

CANDIDATE FOR DYNAMIC CUBES?

Dynamic Cubes Checklist: Data Source


Is the data stored in a star or snow-flake schema?

• If not can it be ported to one

• Use of DB Views to create a star schema are not recommended due to performance reasons

If data is in a star schema, is there referential integrity between dimensions and facts?

• Ignoring this check will result in erroneous totals as you drill up and down the cube

Can the underlying database support execution of multiple queries against a star schema?

• Reports executed against a dynamic cube may result in serial execution of multiple queries

Are most measures semi-aggregate in nature?

• Semi-Aggregate measures are not supported by in-memory aggregates. Manual optimization of in-database aggregates is required

Dynamic Cubes Checklist: Resources


Is there access to resources with DBA skills and privileges

• DBAs are a key resource in the optimal tuning of a dynamic cube

• As data volumes grow and query patterns change, creation of in-database aggregates will be required

Are the personnel developing a Dynamic Cube have advanced modeling/authoring skills?

• DC requires dimensional modeling skills as well as a good understanding of relational star schemas and SQL Queries

• Report developers need to understand how to author reports against dimensional sources

Is the LOB responsible for application maintenance?

• As data volumes grow and more users write reports, a DC will need to be continually optimized. This may be beyond the skill set of the LOB. Unlike Transformer, DCs require optimization across the full stack

Dynamic Cubes Checklist: Change Management


Do you rely on Cognos PowerPlay Studio?

• PowerPlay Studio is only used for Transformer Cubes

• Transitioning to Dynamic Cubes will require a change management strategy for shifting users to Cognos Workspace Advanced

Do you have many Cognos Report Studio reports against Transformer cubes?

• Each report will require conversion to the new Dynamic Cube

• Depending on the complexity of the report and structural differences between the Transformer and Dynamic Cube, this can take 1-3 days per report

CASE STUDY

Summary

Customer requested a POC of Dynamic Cubes in their environment to replace a problematic Transformer implementation

Key Current State Issues

• Transformer Cube takes 20+ hours to build for 3 years of data

• Various smaller cubes and packages were created as a workaround

• Performance using PowerPlay Studio is slow

• Fact table contains 600+ million rows and growing

• Would like to create a cube with 5 years of data for trending analysis

• Slow PowerPlay Studio Reports takes 4+ minutes to render

• Advanced complex reports are maintained by the support group

Case Study


Our Findings…

• This use case could benefit from the use of a Dynamic– Transformer Load Time of 20+ hours goes away to a fully optimized

cube load of 30 minutes

– Performance of a majority of PowerPlay studio reports went from minutes to seconds when hitting in-memory aggregates

• But need to…– Clean up star schema further to resolve RI issues

– Roadmap to optimize star schema with integer keys

– DBA Resources will need to be allocated up-front and on-going

– Continually optimize cube

– Plan for report conversion and change management from PowerPlay Studio to Cognos Workspace Advanced

– Recommend moving to 10.2.2 in order to speed up optimization via the ‘user-defined’ in-memory aggregate feature


Case Study

What we did

• Environment was 10.2.1 FP5

• Analyzed one Transformer cube

• Analyzed the Transformer data sources and validated the underlying star schema

• Modeled one Dynamic Cube against two fact tables. Based the design on how the Transformer cube was structured

• Implemented one virtual cube that combines the two fact tables

• Optimized and validated the cube. Worked with DBAs to create in-database aggregates

• Create 44 in-memory aggregates

• Converted one Transformer based report to Dynamic Cubes in consultation with in-house developer

Case Study


What we determined

Performance

- Queries that hit in-memory aggregates were nearly instantaneous

- Queries that did not hit in-memory aggregates but hit in-database aggregates performed slower but were still in under 10 seconds

- Subsequent request of the same queries performed well due to data cache hits

- Queries that hit the 600M row fact table performed poorly as expected

- After creation of in-database aggregates, load time of in-memory aggregates went from 4 hours to 30 minutes

Optimization

- Several runs of the Dynamic Query Analyzer were required against an adequate workload log to get satisfactory in-memory aggregates.

- The new 10.2.2 ‘user-defined’ in-memory aggregates would have sped up optimization

Case Study


What we determined

Data Source

- Star Schema but:

- No surrogate keys were used

- Dimension level keys were not unique. Some were blank and rolled up to multiple parents

- Later determined referential integrity between facts and certain dimensions was lacking

Data Quality

- Due to referential integrity issues, totals and sub-totals were not footing across various dimensions

- Did not tie back to Transformer totals as Transformer utilized the ‘orphan’ category feature

Report Conversion

- One complex report took approximately 3 days to convert. Required export to XML and search/replace parsing

Case Study


DEMO- WHAT HAPPENS WHEN RI GOES BAD

- COGNOS 10.2.2 USER DEFINED IN-MEMORY AGGREGATES

The following scenario shows a comparison of a Transformer vs. Dynamic Cubes result from a star schema that has a fact row without a corresponding product dimension row.

Transformer Result

- Note that Summary Totals all foot correctly although product summary does not tie to Country Summary

Result of Bad Product Referential Integrity


Transformer Result with Orphan Categories un-supressed



Dynamic Cube Result

- Note that By Country Footer Summary does not add up correctly

- The Dynamic Cube is retrieving Summary Tuples from data cache or in-memory aggs



SPECIAL OFFERS

• Senturus will provide a brief questionnaire to help evaluate if your Cognos implementation is a good candidate to switch from Transformer to Dynamic Cubes

• If you are a good candidate, Senturus will then provide a cost estimate for a Dynamic Cubes proof of concept

Contact [email protected] or 888.601.6010 ext. 85

Dynamic Cubes: Go or No Go Assessment


mailto:[email protected]

Coupon Code: DynCube200

• $200 Off Any Upcoming Senturus Cognos or Tableau Online Training Course

– Options include Dynamic Cubes Cube Designer OLAP Modeling on Sept. 14-15 or Oct. 15-16

• First ten people to use it

http://Senturus.com/Training

Limited Time Offer: $200 Off Online Training


http://Senturus.com/Training

• We are re-releasing this course with important updates that take advantage of new features introduced over the past several product iterations, including:

– Create a Cube from a Framework Manager Package

– Model Relative Time Dimensions

– Command-line tools

– Workload logging

– Updating Cubes in near real-time

– Estimating Hardware Requirements

– Adding dynamic functionality with Parameter Maps

– Using Named Sets with Members

– User-defined in-memory aggregates

UPDATED! IBM Cognos 10.2.2 Dynamic Cubes OLAP Modeling with Cube Designer Course


• Updated course is now available for registration:

– September 14-15

– October 15-16

• Private training, custom course design, and mentoring/prototyping/facilitation services also available

UPDATED! IBM Cognos 10.2.2 Dynamic Cubes OLAP Modeling with Cube Designer Course


*Custom, tailored training also available*

Cognos and Tableau Training Options


Promotion Code: SC15SENTURUS

Save $800 on Registration

• Register by this Monday, August 3, using code SC15SENTURUS, and you’ll receive an additional $100 off of the IBM Insight Super Saver discount rate, for a total saving of $800 off of the standard rate.

Receive a $200 Senturus Training Credit

• By using our code, you’ll also receive a $200 Senturus training credit, good towards any of our live, online Cognos and Tableau training classes.

http://www.senturus.com/ibm-insight-2015/

IBM Insight: Register by Monday, Save $1000!









ADDITIONAL RESOURCES

44

Resources on www.senturus.com


www.senturus.com/events

Upcoming Events


http://www.senturus.com/events

Q&A


Thank You!

www.senturus.com

888-601-6010

[email protected]

Copyright 2015 by Senturus, Inc. This entire presentation is copyrighted and may not be

reused or distributed without the written consent of

Senturus, Inc.

COGNOS DYNAMIC CUBES SET TO RETIRE TRANSFORMER …€¦ · •A ROLAP In-Memory engine that sits on...

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