Date posted: September 21, 2015
In a recent column on CIO, Mike Lamble, CEO of Clarity Solution Group, addresses the movement of enterprise analytics to the cloud. As Lamble notes, it’s not a question of if analytics will move to the cloud, but rather when:
In terms of the Innovation Curve, we’ve moved from “early adopters” to the “early majority.” According to a recent Gartner survey, this year saw a 50 percent jump in the portion of respondents who said they plan to run mission critical applications on the cloud, from about 30 percent in each of the previous four years to 45 percent this year. Many companies – even Fortune 1000 – are mandating that all new infrastructure will be in the cloud.
When technology adoption moves from if to when, the next thing to look for is how. Lamble proposes five project profiles where companies with on-premise enterprise data warehouses (EDW) can look to begin leveraging the advantages of the cloud. They’re worth considering here:
- One time big data projects
Gigabytes to petabytes can be provisioned quickly and brought down when the project is complete whether it’s weeks, months, or indefinitely until the job is done.
- Anything where you might be considering a massively parallel processing appliance
Projects that require terabytes and up— and target tens to hundreds of users rather than thousands— could be a great place to start. Sophisticated appliance IT managers will be delighted by the cloud’s flexibility. You won’t need to pre-pay for capacity, and adding resources is done on a configuration screen as opposed to buying a new rack.
- Machine learning projects
Hadoop’s architecture suits this class of solution, and leading cloud service providers (CSP) offer robust Hadoop distributions and machine learning analytic software.
- Departmental dashboard projects
These are ideal for getting acquainted with a CSP’s solution stack and development nuances. In fact, many of these are already up and running.
- IoT Data Lakes
These include both structured and semi-structured data, with volumes that can be enormous. Data lake projects are often constrained by Hadoop clusters being out of the box for on premise data centers. This isn’t an obstacle for CSPs, whose offerings decouple storage and compute resources in order to economize on storage of less often used data.