Before data can be used to inform decisions, it must be converted into actionable intelligence through what was once a complex and drawn-out multistep process called the data pipeline. Each of the steps in a typical data pipeline might add an hour, many hours, a day or even a week of latency to the process. This latency, or response time, is killing the value of big data, because if you aren’t getting insights in time to act, what value exists in the vast stores of data you are collecting?

In the first of this 3-part series on transforming high performance analytics, we’ll take a look at how current network infrastructures have created a gridlock that stalls big data analysis and needed insights.

To realize the value trapped in data, we must first identify all possible sources of latency throughout the entire data pipeline and IT infrastructure. Then we must take decisive action to eliminate all bottlenecks so that raw, streaming data can be analyzed in place, as it is captured, with as little latency as possible. That isn’t important just for the traditional low-latency use cases such as high-frequency trading or fraud detection applications. The need for real-time intelligence is pervasive, as businesses of all sizes in just about every industry require the ability to analyze data faster and faster.

Because most organizations have historically been constrained by the limited performance and scalability of legacy systems, the average data pipeline has been extended by the very steps taken to improve performance. Time-consuming data consolidation, preparation and performance-tuning steps have introduced untenable decision lags of days or weeks. Dozens of those roadblocks throughout big data ecosystems combine to create a state of gridlock that traps the results of even the fastest data analytics accelerators.

Pervasive Gridlock Is a Result of Legacy Data Analytics Tools and Processes
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To find the root of big data gridlock, look no further than the design of data analytics tools. Most analytics tools are limited by the pervasive compute, storage and I/O bottlenecks inherent in the sequential architectures that make up commodity Hadoop or Spark clusters. That not only limits your capabilities when it comes to searching and analyzing large quantities of dynamic data but also slows the process from what could be seconds of processing time to hours and days.

Artificial constructs like “data lakes” have been added to overcome those limitations. But they have ended up introducing even more latency and needless cost and complexity by requiring data movement to a central location over relatively slow networks already burdened by an increase in traffic. With so many steps and bottlenecks, it’s easy to see why we have reached a state of gridlock that dramatically slows down and even halts intelligence that should be used for decision-making in the moment.

As in public transportation, applying simplistic workarounds to individual infrastructure components isn’t the answer. Acceleration at one point simply pushes more traffic to bottlenecks elsewhere in your infrastructure. Applying GPU and FPGA accelerations to speed a single workload may boost performance in the data center, but 10 Gb/second network speeds will throttle analytics performance to a maximum of 1 GB/second performance in the best scenarios, and even slower when sequential machines like CPUs and GPUs must operate on the data. By itself in isolation, speeding up storage with Flash SSDs will only push more load onto the network, and it does not address slow sequential computing.

It’s Time to Rethink Big Data Strategy and Infrastructure
We can no longer afford to throttle back requirements, simplify queries, tune performance and use other time-consuming hacks to patch together data analytics capabilities. It is time to transform IT infrastructures to deliver instant insights with new, more-advanced high performance analytics solutions that make data exploration faster and effortless, at the edge, in the data center and in the cloud—regardless of data type or structure.

Rethink Data Analytics for Real-time Actionable Insights

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To make real change happen in the world of data analytics, we have to take a more holistic look at addressing each of those issues. We need to use converged infrastructure solutions that address multiple latency problems as a whole while reducing the data pipeline and the burden of data volume on applications, networks and other infrastructure elements. But that doesn’t mean a wholesale overhaul of IT infrastructures. Today’s converged NAS and compute appliances make it easier to embed high performance analytics throughout IT infrastructures to instantly process massive quantities of data in place, delivering insights where they are needed and reducing the amount of data that otherwise must traverse networks.

Eliminate the Gridlock in Your Path to Real-time Insights
Ryft understands that your business can’t be slowed down by insights trapped in legacy network architectures, which is why we created the world’s fastest converged NAS and compute appliance, the Ryft ONE. The Ryft ONE capitalizes on the power of FPGA acceleration with an open API and x86 ease of integration—otherwise known as heterogeneous computing. With this groundbreaking appliance, users can now explore any type of data the moment it is captured without getting caught in the gridlock of current network infrastructures.

In our next post, we will take a deep dive into how these technologies can dramatically speed big data implementations. If you can’t wait until then to see how fast and simple real-time big data analytics can truly be, contact us to learn how your company can navigate the network gridlock and unlock instant insight into all your global data.

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