With large and distributed data sets, storage for big data is a huge challenge. Data can be collected, stored, and distributed from in-house, on-premise, and in-cloud sources. Enterprises that want to take advantage of big data must set the stage with a framework that enables large-scale object storage systems to be deployed. CIOs and data center architects must work with software developers and independent software vendors (ISVs) to deploy an underlying infrastructure that is flexible, scalable, and predictable.

As an aid to those decision-makers, in this report we’ll review the impact of open-source software projects on infrastructure, cost, and support/maintenance and then recommend an approach and strategy that will work best for most enterprises.

Key findings include:

• While open-source storage software offers many advantages, potential customers should balance the criticality of their enterprise data against the emergent nature of open-source solutions.
• A direct link exists between building enterprise value and the competitive advantage big data offers, but there is not necessarily a benefit from “tinkering” with a mature computing infrastructure.
• A major drawback of open-source solutions is that while the software is free, the hardware is not, and neither are support and maintenance, which can total 70 percent of an IT budget. Capex may fall marginally, but opex could rise dramatically.
• Ultimately, open-source software, particularly as it relates to object storage systems, is best used when the open-source project has a specific feature that addresses a glaring issue lessening the enterprise’s ability to generate value or competitive advantage.

Thumbnail image courtesy of: iStock/Thinkstock.

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Now, however, an ecosystem of big data analytics companies is emerging to fill this need. This research paper will examine the evolving big data analytics market and ecosystem and the avenues available to those businesses seeking insight gathered from big data analysis, or what is often called “Insight as a Service.”

Key points examined in this report include:

• Big data as a differentiator to traditional enterprise data warehouses (EDW)
• Insight as the key to unlocking the power of the data and information within an enterprise
• How an enterprise can combat the shortage of big data analytic skills
• The value of actionable analytics
• How emerging big data analytics stores or marketplaces empower business decision makers to make better decisions more quickly
• How Insight as a Service enables end users to leverage the cloud for insight-driven analyses while improving the time-to-sight model

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This adoption comes with a compensatory need for sustainable performance from cloud service providers. Increases in cloud service create a parallel increase in the number of internet users. These users have a growing expectation about improved response times that is augmented with an ever-­expanding trust and reliance on the internet. However, this increased performance cannot be sustained if the corresponding cost to the service provider (SP) for delivering this performance also increases.

What service providers need is a way of delivering low latency, fast response, and increasing performance while minimizing the cost of the network.

This research paper will examine the stress points in a cloud infrastructure and the available network options. Among the key points covered are:

• How networks have realized the performance that can be gained from clustering and parallelism demanded from a server farm today
• The need for performance improvement in both bandwidth and IOPS
• One way data can travel across a network of compute and storage capacity with minimal latency and as close to the speed of the CPU as possible
• Why storage acceleration is critical
• What is causing the rise of the convergent data center
• Three high-­performance network infrastructures that can satisfy the criteria of predictability and repeatability

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