Description of the basic cloud principles, the cost & deployment model for cloud, shortcomings for BI workloads beyond modest scale, some stats on market adoption/preference of cloud for DW.

1. Exploring Cloud Computing Options for
Data Warehousing
July 26, 2012
Mark Madsen
@markmadsen
www.ThirdNature.net

2. Cloud Computing
" a model for enabling ubiquitous convenient on …a model for enabling ubiquitous, convenient, on‐
demand network access to a shared pool of configurable
computing resources (e g networks servers storagecomputing resources (e.g., networks, servers, storage,
applications, and services) that can be rapidly
provisioned and released with minimal managementprovisioned and released with minimal management
effort or service provider interaction."
http://csrc nist gov/publications/nistpubs/800-145/SP800-145 pdfhttp://csrc.nist.gov/publications/nistpubs/800 145/SP800 145.pdf
What people see: seemingly infinite resource to apply to
performance problems on short notice and at low cost

3. Generators: Expensive Product

4. Generators: Commodity Product

5. Generators as a Service: Electricity

6. The Natural Process of Commoditization
Simon Wardley, A Lifecycle Approach to Cloud Computing

7. Managing Hardware Resources
Systems are sized for the peak workload, with the
expectation that it will fluctuate.
Capacity
Demand
Resources
Time

8. Idle resources = low utilizations = money wastedIdle resources low utilizations money wasted
CapacityCapacity
Idle resources
Demand
Resources
Time

9. Not enough resource is (much) worse than too much.
Capacity
Demand
Capac ty
Resources
Time

10. Maintaining capacity just above the peak asMaintaining capacity just above the peak as
workloads increase is the art of capacity planning.
One problem is the large step when upgrading toOne problem is the large step when upgrading to
more resources, equating to a large capital cost.
CapacityCapacity
Demand
Resources
e a d
Time

11. Great performance after an upgrade, badGreat performance after an upgrade, bad
performance at year‐end before the next upgrade.
A steady decline can be worse for user perceptionA steady decline can be worse for user perception
than constant mediocre performance.
Capacityp y
Idle
Demand
Resources
Time

12. What everyone would like: elastic capacity
Pay for the resources you use when you use them,Pay for the resources you use when you use them,
not up front for the entire system that supplies them.
Just like electricityJust like electricity.
Capacityp y
Resources
D d
Time
Demand
Time

13. Five Key Cloud Characteristics
1. On‐demand self‐service
2. Network accessibility
3. Resource pooling
4 Measured service4. Measured service
5. Elasticityy

14. Cloud Architecture
Started with virtual machines
M M M M MMem Mem Mem Mem Mem
Lots of servers, lots of virtual
nodes. But in public clouds:
CPU
Disk
CPU
Disk
CPU
Disk
CPU
Disk
CPU
Disk
• Storage can, often is separated
• VMs don’t run across nodesDisk Disk Disk Disk Disk
• Great for OLTP, not so much for BI
• Implies new software architectures
Memory
p
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
CPUs
Memory
CPUs
Memory
CPUs
Memory
CPUs
Shared disk Shared disk Shared disk Shared disk

15. Database Architecture and the Cloud
Virtualizing on a single server makes
no sense for a database that needs
If your server hardware
environment looks like this:
the full resources.
then it’s probably good for
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Disk
Mem
CPU
Mem
CPU
Mem
CPU
Mem
CPU
Mem
CPU
p y g
lightweight transaction
processing, simple storage and
i l d l
Disk Disk Disk Disk Disk
retrieval, procedural
computations on data.
Memory
If you want to use it for a data
warehouse, you need:
CPUs
Shared disk
• A shared‐nothing database
• A proper storage architecture
• D i li i• Dynamic licensing

16. Three Models of Deployment
2 Leased / hosted2. Leased / hosted
private cloud
1. Public cloud
3. Private cloud

17. Benefits and Rationale
Wh did / id i t th l d?Why did you / are you considering a move to the cloud?
Two primary reasons:
▪ Cost reduction
▪ Reduced time to value
47%
50%
Hardware savings
Pay only for what we use
42%
44%
46%
Lower outside maintenance costs
Lower labor costs
Software license savings Cost reduction
40%
40%
42%
Able to take advantage of latest functionality
Reduce IT support needs
Lower outside maintenance costs
39%
39%
39%
Able to scale IT resources to meet needs
Relieve pressure on internal resources
Rapid deployment
Reduce time to value
39%Resolve problems related to updating/upgrading
IBM global survey of IT and line-of-business decision makers

18. Unexpected Benefits
Speed to deploy:
▪ opex vs capex means faster approvals and
less planningless planning
▪ Provision on‐demand means ability to do all
those small projects that needed resourcesthose small projects that needed resources
and staff to set up
Performance management:
▪ Resource‐oriented fixes done in minutes
▪ Instead of static resources and fluctuations
in performance, set static SLAs and fluctuate
the resources
Administration:Administration:
▪ No more hardware or operating system
upgrades to deal withupgrades to deal with

19. Public Cloud Challenges
1. Multi‐tenant servers and
unpredictable I/O performance
2. Legal problems:
▪ Data co‐mingling in multi‐tenant
databases
▪ Data locality and national laws
3. Cloud compatibility for data
integration and data management
( )tools (environment, data movement)
4. Security requirements
When these are a concern, private clouds
may be the better option today.

20. What are manager preferences?
Prefernottouse cloud
21%
44%
35%
Data warehouses or data
marts
Prefer not to use cloud
Private cloud preference
Public cloud preference
9%
52%
39%
Data mining, text mining, or
other analytics
9%
y
IBM global survey of IT and line-of-business decision makers

21. Comparison of Models

22. New and growing use cases drive the need to expand
The use cases are now interactive applications, lower latency
data, complex analytics and rapidly growing data volumes.

23. Image Attributions
Thanks to the people who supplied the images used in this presentation:
Commoditization diagram – from A Lifecycle Approach to Cloud Computing, © Simon WardleyCommoditization diagram from A Lifecycle Approach to Cloud Computing, © Simon Wardley
tesla coil train ‐ http://www.flickr.com/photos/winterhalter/27364687
Amazon Virtual Private Cloud diagram‐ © Amazon, Inc..
caged_tower_melbourne.jpg ‐ http://www.flickr.com/photos/vermininc/2227512763

24. About the Presenter
Mark Madsen is president of Third
Nature, a technology research and
consulting firm focused on businessconsulting firm focused on business
intelligence, analytics and
information management. Mark is an
award-winning author architect andaward winning author, architect and
former CTO whose work has been
featured in numerous industry
publications. During his career Markpublications. During his career Mark
received awards from the American
Productivity & Quality Center, TDWI,
Computerworld and the Smithsonianp
Institute. He is an international
speaker, contributing editor at
Intelligent Enterprise, and managesg p g
the open source channel at the
Business Intelligence Network. For
more information or to contact Mark,
visit http://ThirdNature.net.

25. About Third Nature
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