The document discusses cloud computing options for data warehousing, emphasizing the benefits such as on-demand self-service, cost reduction, and faster deployment times. It explores different cloud deployment models—public, private, and hosted private cloud—while addressing challenges like multi-tenancy issues and legal concerns. The author advocates for careful capacity planning and the importance of specific database architectures for effective data warehousing in the cloud.

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

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.
Demand
Capacity
Time
Resources

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

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

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

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

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

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

14. Cloud Architecture
Started with virtual machines
Lots of servers, lots of virtual
nodes. But in public clouds:
• Storage can, often is separated
• VMs don’t run across nodes
• Great for OLTP, not so much for BI
• Implies new software architectures

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

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

17. Benefits and Rationale
Why did you / are you considering a move to the cloud?
Two primary reasons:
▪ Cost reduction
▪ Reduced time to value
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 planning
▪ Provision on‐demand means ability to do all
those 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:
▪ No more hardware or operating system
upgrades 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?
9%
21%
52%
44%
39%
35%
Data mining, text mining, or
other analytics
Data warehouses or data
marts
Prefer not to use cloud
Private cloud preference
Public cloud preference
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 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 business
intelligence, analytics and
information management. Mark is an
award-winning author, architect and
former CTO whose work has been
featured in numerous industry
publications. During his career Mark
received awards from the American
Productivity & Quality Center, TDWI,
Computerworld and the Smithsonian
Institute. He is an international
speaker, contributing editor at
Intelligent Enterprise, and manages
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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