COGNOS DYNAMIC CUBES: SET TO RETIRE TRANSFORMER? Update: Pros & Cons

Transcription

1 COGNOS DYNAMIC CUBES: SET TO RETIRE TRANSFORMER? Update: Pros & Cons

2 GoToWebinar Control Panel Submit questions here Click arrow to restore full control panel Copyright 2015 Senturus, Inc. All Rights Reserved 2

3 Presentation Slide Deck on Copyright 2015 Senturus, Inc. All Rights Reserved 3

4 Today s Agenda 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 Copyright 2015 Senturus, Inc. All Rights Reserved 4

5 Introduction: Today s Presenters Pedro Ining Senior BI Architect Senturus, Inc. Jim Frazier Vice President of Client Solutions Senturus, Inc. Copyright 2015 Senturus, Inc. All Rights Reserved 5

6 SENTURUS INTRODUCTION Who we are

7 Senturus: Business Architects for Business Analytics Technology Depth + Business Acumen Business Intelligence Enterprise Planning C-Level Business Acumen Deep Data Experience Predictive Analytics Technical/To ol Expertise Project Management Rigor Copyright 2015 Senturus, Inc. All Rights Reserved 7

8 900+ Clients, Projects, 15 Years Copyright 2015 Senturus, Inc. All Rights Reserved 8

9 Poll Question 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 Copyright 2015 Senturus, Inc. All Rights Reserved 9

10 IBM COGNOS DYNAMIC CUBES: SET TO RETIRE TRANSFORMER? Dynamic Cubes & Transformer Deep Dive

11 Typical Customer Questions 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? 11

12 Current Transformer Architecture Various cubes at different grains and sizes Standard Reports DataMart or Operational Excel Or Text Files Cognos FM Cognos Transformer Cognos Dashboards Ad-hoc Reports Copyright 2015 Senturus, Inc. All Rights Reserved. 12

13 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 Copyright 2015 Senturus, Inc. All Rights Reserved 13

14 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 Copyright 2015 Senturus, Inc. All Rights Reserved 14

15 Cognos BI Stack

16 Dynamic Cubes: Key Architectural Differences 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 16

17 Dynamic Cubes Architecture

18 Dynamic Cubes Aggregate Layers Load time of In-Memory aggregates will depend on performance of the in-database aggregates layer 18

19 Dynamic Cubes Development Lifecycle Key part of Lifecycle 19

20 Dynamic Cubes Product Evolution 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 20

21 Dynamic Cubes Product Evolution 21

22 Dynamic Cubes Support of Transformer Features Relative Time Support Supported Custom Relative time became available in 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 Copyright 2015 Senturus, Inc. All Rights Reserved 22

23 HOW DO I KNOW IF I AM A GOOD CANDIDATE FOR DYNAMIC CUBES?

24 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 Copyright 2015 Senturus, Inc. All Rights Reserved 24

25 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 indatabase 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 Copyright 2015 Senturus, Inc. All Rights Reserved 25

26 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 Copyright 2015 Senturus, Inc. All Rights Reserved 26

27 CASE STUDY

28 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 Copyright 2015 Senturus, Inc. All Rights Reserved 28

29 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 in order to speed up optimization via the user-defined in-memory aggregate feature Copyright 2015 Senturus, Inc. All Rights Reserved 29

30 Case Study What we did Environment was 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 indatabase aggregates Create 44 in-memory aggregates Converted one Transformer based report to Dynamic Cubes in consultation with in-house developer Copyright 2015 Senturus, Inc. All Rights Reserved 30

31 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 user-defined in-memory aggregates would have sped up optimization Copyright 2015 Senturus, Inc. All Rights Reserved 31

32 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 Copyright 2015 Senturus, Inc. All Rights Reserved 32

33 DEMO - WHAT HAPPENS WHEN RI GOES BAD - COGNOS USER DEFINED IN-MEMORY AGGREGATES

34 Result of Bad Product Referential Integrity 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 Copyright 2015 Senturus, Inc. All Rights Reserved 34

35 Result of Bad Product Referential Integrity Transformer Result with Orphan Categories un-supressed Copyright 2015 Senturus, Inc. All Rights Reserved 35

36 Result of Bad Product Referential Integrity 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 Copyright 2015 Senturus, Inc. All Rights Reserved 36

37 SPECIAL OFFERS

38 Dynamic Cubes: Go or No Go Assessment 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 info@senturus.com or ext. 85 Copyright 2015 Senturus, Inc. All Rights Reserved. 38

39 Limited Time Offer: $200 Off Online Training Coupon Code: DynCube200 $200 Off Any Upcoming Senturus Cognos or Tableau Online Training Course Options include Dynamic Cubes Cube Designer OLAP Modeling on Sept or Oct First ten people to use it Copyright 2015 Senturus, Inc. All Rights Reserved. 39

40 UPDATED! IBM Cognos Dynamic Cubes OLAP Modeling with Cube Designer Course 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 Copyright 2015 Senturus, Inc. All Rights Reserved. 40

41 UPDATED! IBM Cognos Dynamic Cubes OLAP Modeling with Cube Designer Course Updated course is now available for registration: September October Private training, custom course design, and mentoring/prototyping/facilitation services also available Copyright 2015 Senturus, Inc. All Rights Reserved. 41

42 Cognos and Tableau Training Options *Custom, tailored training also available* Copyright 2015 Senturus, Inc. All Rights Reserved 42

43 IBM Insight: Register by Monday, Save $1000! 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. Copyright 2015 Senturus, Inc. All Rights Reserved. 43

44 ADDITIONAL RESOURCES 44

45 Resources on Copyright 2015 Senturus, Inc. All Rights Reserved 45

46 Upcoming Events Copyright 2015 Senturus, Inc. All Rights Reserved 46

47 Q&A Copyright 2015 Senturus, Inc. All Rights Reserved 47

48 Thank You! Copyright 2015 by Senturus, Inc. This entire presentation is copyrighted and may not be reused or distributed without the written consent of Senturus, Inc.