Data virtualization in the cloud – accelerating time to-value
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2. DATA VIRTUALIZATION IN THE
CLOUD – ACCELERATING TIME-TO-
VALUE
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Avinash Deshpande
Principal, Big data and Advanced Analytics
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Journey to Cloud
Cloud empowers IT organizations to redefine the way data
services are produced and delivered for Analytics.
▪ more scalable … can reconfigure larger cluster in an hour
▪ more efficient
‒ can turn off over the weekend
‒ can clone prod for UAT and drop when done
▪ more reliable
‒ AWS automatically does 90% of what our DBAs did
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Challenges of traditional warehouse
On Premises Data Warehouse could no longer be extended
to effectively address our evolving business needs:
▪ Growing too fast for Exadata
‒ smallest increase in any resource is a quarter rack
▪ Difficult to set up and tune performance
▪ Difficult to manage usage
‒ Resources usage over time
‒ Queries … impact of each team, process
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DATA VIRTUALIZATION
▪ Business Layer
‒ Keep the business from trolling through the backend database
‒ Data Consistency through single object, multiple consumers
▪ Security thru Data Virtualization rather than every tool
‒ Hard to keep security in synch across multiple analytic tools
▪ Rapid Prototyping
‒ Add new data source in DV layer first
‒ Move to Redshift / Pentaho after virtual analytics are validated
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BENEFITS
▪ Proactive – IT has embraced cloud as a model for achieving
innovation through increased efficiency, reliability and agility
▪ Reusability and template development
▪ Rapid innovation within governance structure, balanced
costs, risks and service levels
▪ Greater efficiency and reliability, enabling broader audience
to consume IT services via self-service
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NEXT STEPS
Want to learn more?
▪Amazon AWS - https://aws.amazon.com/
▪Data Virtualization
https://en.wikipedia.org/wiki/Data_virtualization
http://www.denodo.com/en
▪Columnar databases - https://aws.amazon.com/redshift/
▪Pentaho DI and BA - http://community.pentaho.com/