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Redefining Analytics by Migrating Data to Cloud

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One of the critical factors that influence decision making when it comes data project initiative is the additional infrastructure that needs to be housed in the data center. Unlike other IT projects, data initiatives always require more compute and storage - two most critical components of an IT infrastructure. In addition to that is licensing cost for tools, etc. However, today with the arrival of cloud, vendors provide a variety of offerings "as-a-service" like PaaS, IaaS and Data Warehouse as a service.

The Elastic Nature of Cloud

One of the major benefits of moving to a cloud infrastructure model is the option to scale as and when required, without investing upfront. Along with that, an architect can chose and shop a particular component on demand. For example, when there is a spike in workloads, the architect can choose to scale only the computing capacity. In case of read only data, read replicas (available as a feature) could be used without any further data copy effort. Also, setting up a cluster of nodes for a heavy data crunching job can be done with very few configuration steps.

Opportunity to Redefine the Current State

Most of the existing EDW customers have invested or continue to invest in "on-premise" data appliances, primarily because it gives enormous processing power for workloads. But given the benefits of moving to a cloud setup, enterprises can re look at the current state and make an inventory of numerous things such as jobs that require more computing power, data that can rest, data which needs to be cached, data that requires to be processed closer to where it stays, performance oriented contextual data and more. Such a classification helps during migration. It significantly helps to decide what resources need to be reserved on cloud and which ones can be built on-demand? There are also platforms to support data management and analytics solutions built only for cloud.

Cut to the bigger picture, a cloud setup not only gives elasticity, but also gives the data architect the flexibility to build data environment which is scalable, futuristic, and cost effective and essentially an architecture which is very native to cloud principles.

About Author

Pon Prabakaran Shanmugam- Principal Consultant, Analytics Practice, Wipro, Ltd.

Pon Prabakaran Shanmugam is a Principal Consultant with Wipro Analytics practice. He possesses exhaustive data architecture experience in the Financial Industry with strong data modeling, integration and analytical skills, and is an enthusiastic agile modeling proponent. He is also a strong believer of embracing open source technologies to make data architecture flexible and evolving.

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