Big Data Advantage, part 1: “It’s down there somewhere. Let me take another look.”

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Probably like many of you, the Cray team likes to debate and muse about industry trends when we meet over lunch or drinks. I enjoy our banter – it’s part of what makes it so much fun to work at Cray. In that spirit, I wanted to share some of what I’ve found most interesting in a few posts about what I call “big data advantage.” By now, we’re pretty used to hearing about the potential of big data: new breakthroughs, unforeseen opportunities and disruptive industry impact. And although there have been serious advancements, most businesses still struggle to take full advantage of big data. In fact, I’m sometimes reminded of a line from “The Big Lebowski,” where the Dude says, “It’s down there somewhere. Let me take another look.” So ... [ Read More ]

Threat Intelligence – Fool’s Errand or Holy Grail?

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In recent years, we’ve seen a disturbing trend in computer network defense — attackers are innovating at a much faster pace than defenders can keep up with. We’ve seen malware evolve from primitive code used primarily by the advanced criminal element to commercialized services available to anyone with the financial means to purchase them. A variety of kits are available on the deep web and dark web for anyone looking to initiate an attack. Many attackers reuse tactics, techniques and procedures (TTP), adapting their code over time to keep ahead of both security analysts and the anti-virus and anti-malware industry. As the frequency of attacks continues to increase, so does the likelihood that an organization has seen the attack ... [ Read More ]

Urika-GX: A Game Changer for Big Data Analytics

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There’s a lot of hype around big data in healthcare and the life sciences. But big data is here to stay. Information is what drives the entire industry. When I worked in big pharma, I learned that the product of a pharmaceutical company is not a pill, it's a label. And a label is just a specific assemblage of information, carefully distilled from terabytes of information collected and analyzed over the course of many years by many intelligent people. To compete, companies have to be very good at turning data into information, and information into knowledge. The stakes couldn't be higher, because every day millions of patients rely on the quality of this data and the strength of the analyses done by researchers. Analyzing big data is ... [ Read More ]

Graph Databases: Key Thoughts from Online Chat

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It’s pretty interesting to see graph analytics gain traction in the work of big data. We’ve been focusing on graph databases to round out Hadoop® and Spark™ ecosystems and allow for more advanced analytics — and enable people to uncover never-before-seen patterns. (Tell me that’s not cool!) From solving real-world problems such as detecting cyberattacks and creating value from IoT sensor data to precisely identifying drug interactions faster than ever before, graph has become a powerhouse in looking at complex, irregular and very large datasets to identify patterns in near real-time. On March 16, we hosted an online chat titled “Graph: The Missing Link in Big Data Analytics” with industry experts from Deloitte and Mphasis. Sixty-one ... [ Read More ]

How CGE Achieves High Performance and Scalability

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In our graph series so far, we have explored what graph databases are and when they are valuable to use, as well as the Cray Graph Engine (“CGE”), a robust graph solution. For this last installment, we dive into how hardware affects the performance of a graph database. Cray’s main product line, the XC™ series, is mostly used for scientific computing. From the point of view of an applications programmer, there is an important difference between scientific computing and the kind of computations done on a graph database. Programmers call it spatial locality. In a nutshell, if a computation has a lot of spatial locality, when a computation has to fetch some value from memory, the next value it’s going to need is usually stored nearby in the ... [ Read More ]