What’s the problem with Big Data? You guessed right — it’s BIG.
Big Data empowers organizations to discern patterns that were once invisible, leading to breakthrough ideas and transformed business performance. But there is simply so much of it, and from such myriad sources — customers, competitors, mobile, social, web, transactional, operational, internal, external, structured, and unstructured — that, for many organizations, Big Data is overwhelming. The torrents of data will only increase as the Internet of Everything spreads its ever-expanding wave of connectivity, from 10 billion connected things today to 50 billion in 2020.
So, how can organizations learn to use all of that data?
The key lies not in simply having access to enormous data streams. Information must be filtered for crucial, actionable insights, and presented to the right people in a visualized, comprehensible form. Only then will Big Data transform business strategies and decisions. In effect, Big Data must be made small.
However, as McKinsey & Co. reported, many organizations don’t have enough data scientists, much less ones who understand the business well enough to draw conclusions. The trick is to get the scientists together with the experts who understand the business levers driving the organization. Put them in a room with the right tools, and watch the synergy fly.
But what sort of a room?
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Tags: Big Data, Cisco, Cisco Consulting Services, data scientists, innovation, Internet of Everything, internet of things, IoE, IoE Value Index, IoT, retail, value at stake
Following part one of our Big Data in Security series on TRAC tools, I caught up with talented data scientist Mahdi Namazifar to discuss TRAC’s work with the Berkeley AMPLab Big Data stack.
Researchers at University of California, Berkeley AMPLab built this open source Berkeley Data Analytics Stack (BDAS), starting at the bottom what is Mesos?
AMPLab is looking at the big data problem from a slightly different perspective, a novel perspective that includes a number of different components. When you look at the stack at the lowest level, you see Mesos, which is a resource management tool for cluster computing. Suppose you have a cluster that you are using for running Hadoop Map Reduce jobs, MPI jobs, and multi-threaded jobs. Mesos manages the available computing resources and assigns them to different kinds of jobs running on the cluster in an efficient way. In a traditional Hadoop cluster, only one Map-Reduce job is running at any given time and that job blocks all the cluster resources. Mesos on the other hand, sits on top of a cluster and manages the resources for all the different types of computation that might be running on the cluster. Mesos is similar to Apache YARN, which is another cluster resource management tool. TRAC doesn’t currently use Mesos.
The AMPLab Statck
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Tags: AMPLab, analytics, BDAS, Big Data, BlinkDB, Cisco, custom, database, Hadoop, innovation, mapreduce, Mesos, NoSQL, Scala, security, Shark, Spark, Stack, TRAC, TRAC Big Data Analysis
Recently I had an opportunity to sit down with the talented data scientists from Cisco’s Threat Research, Analysis, and Communications (TRAC) team to discuss Big Data security challenges, tools and methodologies. The following is part one of five in this series where Jisheng Wang, John Conley, and Preetham Raghunanda share how TRAC is tackling Big Data.
Given the hype surrounding “Big Data,” what does that term actually mean?
John: First of all, because of overuse, the “Big Data” term has become almost meaningless. For us and for SIO (Security Intelligence and Operations) it means a combination of infrastructure, tools, and data sources all coming together to make it possible to have unified repositories of data that can address problems that we never thought we could solve before. It really means taking advantage of new technologies, tools, and new ways of thinking about problems.
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Tags: analytics, API, Big Data, Cisco, database, Hadoop, HDFS, innovation, Intelligence, java, mapreduce, NoSQL, operations, security, Shark, Spark, SQL, telemetry, TRAC, TRAC Big Data Analysis
On a typical day, we leave a vast trail of data in our wake. Our browsing histories, online preferences, shopping habits, work decisions, social interactions—all are rendered in binary code, prompting a complex interaction of requests, responses, affirmations, and denials.
And that’s just from our laptops and smartphones.
What about when the Internet of Everything — with its explosion in connectivity from 10 billion “things” today to 50 billion in 2020 — truly shifts into overdrive? At that point, our clothing, our houses, our cars, our lawns, and our refrigerators may be generating ever-larger torrents of data — all about us.
This upsurge in personal Big Data has big implications. Indeed, each person’s emerging digital persona will go a long way toward defining their place in the world. Furthermore, all of that data already has great intrinsic value to Internet giants, retailers, financial services companies, and many others. If we manage it right — in what I see as a burgeoning Marketplace of Me — some of that value may come right back to us.
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Tags: Big Data, Cisco, Cisco Consulting Services, innovation, Internet of Everything, IoE, IoE Value Index, IoT, privacy, security, value at stake
Dreamhack Winter 2013 in the small city of Jonkoping in central Sweden claims to be the world’s largest digital event. I was skeptical at first, but when I arrived for the event, I was completely amazed at the scale of things in this tiny part of the world where its only bright for 5 or 6 hours during day this time of the year.
Of course, the amount of daylight didn’t impact one bit the large numbers of people streaming into the event venue well before opening time on Thursday afternoon.
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Tags: analytics, Connected, connected mobile experiences, connectivity, Dreamhack, innovation, internet, jonkoping, location, location analytics, location based services, Millennial, mobile, mobility, network, platform, telia, wi-fi, wi-fi location, wifi, wireless