In today’s fast-paced competitive market, there are critical advantages to utilizing infrastructure-as-a-service, especially for businesses concerned about fast-tracking their analytics infrastructure. IaaS goes beyond simply virtualizing infrastructure and adds a software management layer to allocate resources, either automatically or by a service provider’s staff, with the goal to maximize hardware efficiency.
Benefits of IaaS
The StateTech team summarized key drivers for the meteoric growth in infrastructure as a service (IaaS):
The last thing a CIO wants to worry . . . is whether the infrastructure can keep pace with the need to innovate and respond to competitive pressures. IaaS is a good solution to the problem in many ways. It can reduce infrastructure costs, provide virtually limitless scalability and agility, and accelerate time to market. And it does this in a model that virtually ensures uptime . . . and the highest levels of security and compliance.
The hoopla surrounding IaaS is sufficiently robust to make enterprise decision-makers question whether they are “riding the IaaS wave” as they should. The Benefits of IaaS model include:
- Cost savings - at the first sight, cost-savings are not that obvious, as whatever you save through offloading tasks you will end up paying for the service. However, cost saving is going to be tangible, although not necessarily direct. Overall costs are bound to go down- you will require less people, streamline your operations and have time and resources to focus on business growth.
- Cutting edge technology- Because of their own competitive pressures, IaaS suppliers are compelled to offer the latest and fastest technologies.
- IaaS saves IT staff time - IaaS offloads some of the tasks that your IT team would ordinarily need to deal with. In examining time savings, it is important to factor in the benefits of automation that some service providers offer. In some cases, automation will not only offset the operational costs, but also optimize the way your data is loaded and saved. This automated optimization can save a lot of money in direct storage and compute costs, as well as the time spent on data prep, wrangling and munging by your staff. This is precisely where the automation of a self-optimizing data management platform like Panoply.io can prove so useful, as it can eliminate the overhead of preparing and modeling data, and of managing cloud infrastructure.
- Focus on business growth – The savings in staff time and resources noted above also enable an important shift in focus, allowing your team to keep critical business imperatives front-and-center.
- Scalability and elasticity – IaaS provides an extraordinary level of flexibility and scalability in response to an enterprise’s requirements. While there remains an element of difficulty under some IaaS systems with respect to scalability, self-optimizing functionality of Panoply in managing cloud infrastructure proves useful to mitigate that complexity.
- Support for Disaster Recovery (DR)/Business Continuity (BC) – IaaS services provide high-level, consolidated DR/BC solutions, thus reducing costs and increasing manageability.
Making the first step to IaaS
Broadly speaking, IaaS market consists of three generations of service providers. Starting with the veterans, such as SAP, EMC, DELL, IBM. The initial successes by pioneers of cloud computing, as IBM Canada's cloud business unit leader Mark Noppe points out, “are being followed by the adoption of platform as a service (PaaS),” which eliminates the need for organizations to build and maintain the infrastructure traditionally used to develop applications.
The second generation of cloud service providers includes goliaths like Amazon and Google, as well as Microsoft and a few others, who offer multiple cloud solutions for practically everything. While a broader comparison of the various cloud options is beyond the scope of this blog, Panoply did conduct its own in-depth comparisons between Amazon’s Redshift and Google;s BigQuery. In line with the conclusions of Gartner’s Magic Quadrant analysis, Panoply found Amazon Redshift to deliver significantly superior results for usability, performance, and cost for almost all analytical use-cases, especially at scale.
As discussed in a blog by Roi Avinoam, Panoply’s co-founder and CTO:
The only critical apparent drawback of Redshift is its relative complexity as it requires constant low-level tuning of the virtualized hardware and database configurations. This apparent complexity is double edged as it can be seen as greater flexibility that allows us to fine-tune Redshift to our specific needs which will result in an even greater advantage in performance and cost.
The third generation of IaaS providers is all about automation and managing complexity by helping businesses optimize with the help of machine learning and the latest developments in artificial intelligence. This last generation includes companies like Cloudera offering hadoop as a service, CognitiveScale who leverages machine learning to interpret Big Data and provide insights, and Panoply, the self-optimizing analytics infrastructure in the cloud. These technologies aim to abstract away the vast complexity of technologies, components and configurations required to maintain a robust analytics infrastructure allowing companies to instantly utilize their data.
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