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Tag: big data

PSSC Labs Announces Hadoop Big Data Servers with Intel Xeon Scalable Processors

By marketing@site-a.com

Custom-built and certified compatible with leading Hadoop distributions from Cloudera, Hortonworks & MapR

LAKE FOREST, CALIF., JANUARY 31, 2018 /PRWEB/ — PSSC Labs, a developer of custom HPC and Big Data computing solutions, today announced a new version of its CloudOOP 12000 cluster server integrated with Intel’s newest Xeon® Scalable Processors to deliver breakthrough performance for handling big data tasks such as real-time analytics, virtualized infrastructure and high-performance computing.

In additional to advanced architecture, the Intel new processors feature a rich suite of platform and security innovations for enhanced application performance including Intel AVX-512, Intel Mesh Architecture, Intel QuickAssist, Intel Optane SSDs and Intel Omni-Path Fabric.

The CloudOOP 12000 is the only server specifically designed for Hadoop, Kafka, Big Data and Internet of Things (IoT). It offers 2x the density and up to 35% lower power draw than traditional manufacturers, as well as a near 50% increase in data throughput performance. Reducing power draw means a lower datacenter footprint and a significantly reduced total cost of ownership with an over 90% efficiency rating.

Organizations can custom build CloudOOP 12000 servers from the company’s new EZ System Configurator https://www.site-a.com/servers/big-data-servers/#chassis. Every system is ready-to-run on delivery and comes with all necessary hardware, software, networking, integrations, as well as support from PSSC Lab’s US-based team of experienced engineers. Each CloudOOP 12000 is also customized with the Hadoop distribution of choice.

CloudOOP 12000 big data servers feature: 

  • Support for Intel Xeon Scalable Processors. Intel Xeon Scalable Processors deliver an overall performance increase up to 1.65x from the previous generation, and up to 5x OLTP warehouse workloads versus the current install base.
  • OS installed on separate SSD hard drive(s) to increase system speed, reliability and usability support for Red Hat, CentOS, Ubuntu and most other Linux distributions, as well as Microsoft Windows
  • Supports up to 512GB DDR memory modules featuring Error Correcting (ECC) support that automatically detects and corrects memory errors
  • Redundant 90%+ Energy Efficient Power Supplies
  • Redundant RAID 1 Mirror Operating System Hard Drives
  • Up to 144TB storage with support for SSD, SAS and SATAIII
  • Configurable to RAID 0, 1, 5, 10 or JBOD
  • Dual GigE network bandwidth comes standard, with 10GigE, 40GigE, 100GigE and Infiniband network connectivity possible. Also, optional network integration with Intel OmniPath Architecture.

“Our CloudOOP 12000 series not only reduces both capital expenditure and operating expenses but offers superior data I/O performance for real-time processing,” said Alex Lesser, Executive Vice President of PSSC Labs. “It’s the perfect pre-configured server for a variety of applications across government, academic and commercial environments including Design & Engineering, Life Sciences, Physical Science, Financial Services and Machine/Deep Learning.”

Every PSSC Labs server and cluster comes with three-year unlimited phone / email support package (additional support available) with all support provided by a US-based team of experienced engineers. Pricing for a CloudOOP 12000 with Intel Xeon Scalable processors start at only $2,500.

PSSC Labs services with every custom-build include: 

  • Operating system installation
  • Storage partitioning and configuration (JBOD or RAID)
  • Net Connect integration service for easy network boot
  • BIOS customization
  • Power and cooling consultation prior to delivery
  • Mac address recording and reporting
  • Node name labeling
  • Rack ‘n Roll Services Available for Turn-Key Installation

 Source: http://www.prweb.com/releases/2018/01/prweb15135188.htm

PSSC Labs Featured in Becker’s Hospital Review: Is cloud computing the best option to handle the life sciences data avalanche?

By marketing@site-a.com

While cloud services are becoming more sophisticated and customizable, they may fall short in handling the “data avalanche” that the life sciences industry is grappling with.

Data sets that are too large and complex for traditional data processing methods to handle are a sticky issue in the sciences, perhaps no more so than in the life sciences industry. From biotechnology to pharmaceuticals to medical device manufacturers, private-sector organizations and research institutions spend their days performing research and development tasks that produce terabytes of raw data gleaned from clinical results, disease states, scientific studies, and individual patient data.

This “data avalanche” must then be sorted, analyzed, and distilled into actionable information so that new medications, biomedical devices, and other products can be developed, refined, tested, and approved for sale as quickly as possible. Because of the gravity of the decisions that depend on this information — human lives are literally at stake – life sciences companies operate under some of the closest scrutiny and strictest regulations in the world.

To cope with the data avalanche and compete in an increasingly hypercompetitive global marketplace, many life sciences organizations are considering cloud computing solutions to increase their data processing power and storage capabilities. However, cloud solutions can often fall short in big data processing capabilities and result in cybersecurity issues, eye-popping monthly bills, and other problems.

Big Data = Big Cloud Bills
Cloud providers are quick to tout the alleged cost savings of using their services over purchasing in-house IT infrastructure. They claim that customers do not have to come up with large capital investments upfront and pay only for what they use, as they go. This works out well for some companies, particularly small start-up firms that are cash-poor and that aren’t dealing with very large data sets or highly complex computations.

However, cloud services such as AWS are notorious for sky-high monthly bills filled with hidden “gotchas,” especially for companies that require a lot of computing power. Deep Value, which develops complex research-driven trading algorithms, decided to run some numbers once their AWS bills began to exceed $70,000/month. In the end, they discovered that using AWS was 380% more expensive than purchasing their own high-performance computing equipment.

In addition to unexpected line items on their monthly invoice, cloud customers can also be hit hard by the indirect costs of performance problems and cyberattacks.

Cloud Performance May Not Be Up to Par
In the life sciences, the reliability and uptime of mission-critical systems are paramount, and a key marketing point of cloud services is the idea that customers don’t have to worry about maintaining their own equipment. Yet as cloud computing grows in popularity, cracks are appearing in its foundation. In February 2017, AWS suffered an outage that was so bad, it couldn’t get into its own systems to communicate with the throngs of customers that were knocked offline – all due to a misconfiguration on the part of an AWS employee.

The cloud doesn’t necessarily beat in-house infrastructure in the performance category, either, especially when processing enormous data sets. Cloud service providers typically run multiple servers in different locations, which can cause very serious latency issues when transferring large data sets and performing the highly complex calculations that life sciences companies run all day long.

The Dark Cloud of Cybersecurity Concerns
Over the past few months, an epidemic of AWS breaches impacted organizations large and small, including Verizon, the Republican National Committee, and a company called Talent Pen that processed job applications containing the personal information of thousands of Americans who held Top Secret security clearances. All of these breaches were due to the affected organizations (or their third-party vendors) not having configured their AWS security settings properly.

Cloud security settings can be very tricky, but even if an organization gets them right, they can still be hacked through no fault of their own. Because so much valuable data from so many different organizations is being migrated to the cloud, data centers have become highly attractive targets for hackers. Financial regulators and the tech industry are so concerned about the possibility of a major attack on AWS that in the wake of the February outage, they sounded the alarm over what they deem an over-reliance on the AWS service by the organizations that form the bedrock of American society, particularly financial companies.

The cloud security risk to life sciences organizations is three pronged: a cyberattack that brings down their cloud could leave them unable to operate; invaluable market research and digital intellectual property could be stolen by competitors or foreign governments; and a hack could mean running afoul of a myriad of government regulations and being hit with millions of dollars of fines and lawsuits.

In-House IT Infrastructure Means Optimum Customization & Control
Finally, cloud services suffer from customization and control issues. When using a cloud service, the ability to make changes is quite limited. Services such as AWS offer a menu of items that fit most organizations’ needs – unless those needs are highly specialized. Organizations that own their own computing equipment have complete control over their data environments and can act quickly to make adjustments or implement new features.

Rather than rushing to migrate to the cloud, certain life sciences organizations might be better served by investing in their own high-performance computing equipment to reduce their costs, improve their cyber security, and make the data avalanche work for them to accelerate innovation. Only a true comparison between the two can shed light on which option works best.

Source: https://www.beckershospitalreview.com/healthcare-information-technology/is-cloud-computing-the-best-option-to-handle-the-life-sciences-data-avalanche.html

Welcome to the Age of “Big Intelligence”

By marketing@site-a.com

Everyone knows the scenario – after years of development and advancements, machines imbued with Artificial Intelligence somehow become self-aware without the knowledge of their human creators and end up destroying humanity as we know it. It’s a crazy premise, but if you listen to Tesla and SpaceX CEO Elon Musk and other futurists, you’ll hear over and over that Big Intelligence is something to fear.

Science fiction movies have a habit of predicting doom and destruction, but the truth is we don’t have to worry about this dystopian world where AI becomes something man cannot control. True, there could be unpredictable social consequence much like those brought about by the rise of social media. But in terms of actual takeover and destruction, the odds are slim to none. Rather than fretting about killer robots, it’s time to realize that the AI revolution is actually the proliferation of “Big Intelligence” and its future is much more benign. Big Intelligence is where we are today in terms of automation, robotics and computing – and it bears little similarity to the sentient machines most people think of when they hear AI. What’s more, fear of AI shouldn’t hinder the legitimately useful work Big Intelligence can help complete.

AI is Big Intelligence in Disguise

Many companies like to tout the AI capabilities of their offerings, but much of the literature you’ll find is nothing more than marketing gimmick. The technology being commercialized or used in research is not really AI, but simply better programming and faster data crunching fueled by advancements in hardware. While futurists like to envision the all-knowing, self-aware machine, Artificial Intelligence or machine learning is not a substitute for human intelligence, and even the most advanced machines are far from substituting a human brain.

With the convergence of large datasets with faster computers and better code to process the data, Big Intelligence has progressed over the past decade due to real technology advancements that allow us to collect and process data at an ever increasing rate, and it’s what powers most AI platforms today. But the key here is not some technology that can replace human intelligence – rather, AI as it currently stands is a set of tools that merely help us better process, interpret, and understand the mass amounts of data companies gather from actual thinking humans. It can help businesses better predict their own and their customers’ needs, to better optimize and even conserve resources.

Hardware is Critical to Big Intelligence 

The only reason we are discussing the possibility of AI is due to recent advancements in computing performance. If the AI hardware could not process data in near real-time, things like self-driving cars, automated logistics centers, operating systems that are virtual and learn through association would still be figments of our imagination.  However, ultimately these so-called AI data still rely on constant data input from humans, without which they could not function.

Artificial Intelligence is a misnomer. Most of what we consider AI is really a high-performance computer (HPC) crunching a massive amount of data, which does not have intelligence the way a human brain does to conceptualize, reach its own conclusions and think for itself. What it can do is improve computing processes through automation, but the end result of most automated processes still require human supervision.

Computers can now process data in real time, but all that processing power is useless if people feed the machine bad data from the get go or don’t know what to do with all that information and analysis once they have it. Cognitive solutions that leverage AI can provide explanations, recommendations, and inform what future actions or outcomes might be required via their predictive nature, but it’s still a human who is feeding the beast.  We are still the “intelligence” behind AI – the artificial part is being able to crunch data at a scale and time-span humans can’t achieve.

The promise of AI has been around a long time, but never went anywhere because hardware could not sustain that much data analysis.  No one could capitalize on the concept. That is not the case today. Hardware has advanced to such a degree that for each new automation concept there is a company that builds the hardware necessary to realize the idea.  Super-fast multi-core processors or massive storage devices are tomorrow’s recycling candidate. Cloud computing, virtualization, faster processors – all make up the core technology of what we call AI.

In addition, the amount of data companies now churn out is almost unfathomable. Almost everything we touch is sending data to someone from smartphones, to the Internet, to online processes, to set-top boxes, and on and on. Technology is everywhere and every bit of it is a data source. At the convergence of all this Big Data and mock AI technology is nothing that resembles actual sentience – it’s simply Big Intelligence. We’ll continue to see it improve as the convergence of better programming through High Power Computing and Big Data and companies traditional working with one or the other begin to bring the two together in ever more creative applications.

We are living in a truly exciting time of data and high-performance computing. We now have the ability to take advantage of data at scale and analyze that data in real time, but we shouldn’t let the misnomer of AI scare us away from this pursuit. Let’s call Big Intelligence what it is and enjoy the power more data and advanced hardware has bestowed on the human race.