The massive computing and storage resources that are needed to support big data applications make cloud environments an ideal fit. Now more than ever, there is a growing number of choices of cloud infrastructure providers, from Amazon AWS, OpenStack offered by the likes of HP, Rackspace and soon even Dell, VMware vCloud as well a... INCLUDING - Effectively managing your Hadoop stack in any data center (on-premise, cloud, hybrid…) - Maintaining the flexibility to choose the right cloud for the job in an ever-changing environment - Consistently manage your hadoop deployment with other elements of your Big Data system such as NoSQL DB, Web Tier etc.

2. Columbus & The Cloud

5. http://code.zynga.com/2012/02/the-evolution-of-zcloud/

6. http://code.mixpanel.com/2010/11/08/amazon-vs-rackspace/

11. Realization:
What You
Really Care
about Is
App
Portability

14. http://techblog.netflix.com/2012/07/benchmarking-high-performance-io-with.html

16. A Typical Big Data App…

17. • Auto start VMs
• Install and configure
app components
• Monitor
• Repair
• (Auto) Scale
• Burst…

18. Making the
deployment,
installation, scaling, fail-
over looks the same
through the entire stack

19. Running Bare-Metal for
high I/O workload, Public
cloud for sporadic
workloads ..

20. • Available under
different distributions
• Cloudera
• IBM BigInsights
• MapR
• Hortonworks

22. • Run on Any Cloud
Putting • Consistent MGT
• Dynamic Scaling
Cloudify and
• Auto Recovery
Hadoop • Auto Scaling
Together • Role Assignments
• Monitoring
• Simple maintenance

23. How it works..
1 Upload your recipe.
2 Cloudify creates VM’s & installs agents
3 Agents install and manage your app
4 Cloudify automate the scaling

24. Few Snippets..

25. Demo Time..

26. http://www.cloudifysource.org
http://github.com/CloudifySource
https://github.com/CloudifySource/cloudify-
recipes/tree/master/services/biginsights