Every day, security threats continue to evolve, as cyber attackers continue to exploit gaps in basic security controls. In fact, the federal government alone has experienced a 680% increase in cyber security breaches in the past six years, and cybersecurity attacks against the US average 117 per day. Globally, the estimated annual cost of cybercrime is over $100 billion. Often, even when security breaches are identified, it can be extremely difficult to figure how they happened or who is responsible.
One company working hard to prevent these threats is Solutionary, a managed security services provider (MSSP) that actively monitors their customers’ technology systems in order to identify and thwart security events before any negative impacts occur.
In order to provide real-time analytics of client traffic and user activity, Solutionary, a wholly owned subsidiary of NTT Group, developed a patented Solutionary ActiveGuard® Security and Compliance Platform which correlates data across global threats and trends in order to quickly identify security alerts and provide clients with actionable alerts.
The patented, cloud-based ActiveGuard® Security and Compliance Platform is the technology behind Solutionary Managed Security Services
In order to keep up with growing data volumes, the need for fast security analytics, and their expanding client base, Solutionary needed to find a way to quickly scale their infrastructure, as their traditional server infrastructure was not able to easily scale and support in-depth analysis. Their challenge was to figure out how to:
1) Increase their data analytics capabilities and improve their clients’ security
2) Cost-effectively scale as their clients/data volume grows
When a security threat occurred in the past, the legacy systems could only be used to analyze log data; they couldn’t see the big picture. Thus, when an event happened, it would sometimes take weeks of forensics work to figure out what had occurred. In order to meet these challenges, Solutionary turned to the MapR Distribution for Hadoop running on the Cisco Unified Computing System™. By using Hadoop, Solutionary was able to smoothly analyze both structured and unstructured data on a single data infrastructure, instead of relying on a costly traditional database solution that couldn’t pull in both structured and unstructured data into a single platform for analysis.
Cisco UCS Common Platform Architecture for Big Data
Specifically, the Cisco/MapR environment consists of two MapR clusters of 16 Cisco UCS C240 M3 Rack Servers. Solutionary uses the Cisco UCS Manager to provision and control their servers and network resources, while the Cisco UCS 6200 Series Fabric Interconnects provide high-bandwidth connections to servers, and act as centralized management points for the Cisco infrastructure, eliminating the need to manage each element in the environment separately. Because of the environment’s high scalability, it’s easy for the fabric interconnects to support the large number of nodes needed for MapR clusters. Scalability is improved even further by using the Cisco UCS 2200 Series Fabric Extenders to extend the network into each rack.
Cisco UCS Components
With MapR and the Cisco UCS CPA for Big Data environment, Solutionary can now access a much greater amount of data analysis and contextual data, giving them a more informed picture of behavior patterns, anomalous activities, and attack indicators. By quickly identifying global patterns, Solutionary can identify new security threats and put them into context for their clients.
Let me know if you have any comments or questions, or via twitter at @CicconeScott.
Tags: Big Data, blade server, blades servers, C240 M3 Rack Server, Cisco UCS, Cisco Unified Computing System, Cisco Unified Data Center, Cisco Unified Fabric, Hadoop, MapR, rack server, Solutionary, UCS Central, UCS service profiles
Big Data remains one of the hottest topics in the industry due to the actual dollar value that businesses are deriving from making sense from tons of structured and unstructured data. Virtually every field is leveraging a data-driven strategy as people, process, data and things are increasing being connected (Internet of Everything). New tools and techniques are being developed that can mine vast stores of data to inform decision making in ways that were previously unimagined. The fact that we can derive more knowledge by joining related information and recognizing correlations can inform and enrich numerous aspects of every day life. There’s a good reason why Big Data is so hot!
This year at Hadoop Summit, Cisco invites you to learn how to unlock the value of Big Data. Unprecedented data creation opens the door to responsive applications and emerging analytics techniques and businesses need a better way to analyze data. Cisco will be showcasing Infrastructure Innovations from both Cisco Unified Computing System (UCS) and Cisco Applications Centric Infrastructure (ACI). Cisco’s solution for deploying big data applications can help customers make informed decisions, act quickly, and achieve better business outcomes.
Cisco is partnering with leading software providers to offer a comprehensive infrastructure and management solution, based on Cisco UCS, to support our customers’ big data initiatives. Taking advantage of Cisco UCS’s Fabric based infrastructure, Cisco can apply significant advantage to big data workloads.
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Tags: ACI, Big Data, blade server, Blade Servers, Cisco UCS, Cisco UCS C240 M3 Rack Server, Cisco Unified Computing System, Cisco Unified Data Center, Cisco Unified Fabric, Cloudera, Hadoop, Hortonworks, MapR, rack server, UCS Central, UCS service profiles
The Cloudera Sessions Roadshow helps companies to navigate the Big Data journey. As Hadoop takes the data management market by storm, organizations are evolving the role it plays in the modern data center. This disruptive technology is quickly transforming an industry, the value it adds to the modern data center, and how you can leverage it today. When combined with Cisco Unified Computing System™ (Cisco UCS®), the joint solution helps you exploit the valuable insights contained in your data to drive meaningful change in your business.
The Cloudera Sessions roadshow is designed to help organizations to identify where they are on their Big Data journey and to navigate how to stay the course in a low-risk, productive way. The Cloudera Sessions’ attendees will benefit from hearing about Cloudera and its partners’ experiences with real-world deployments, as well as those of Hadoop users who plan and manage them.
Cisco is partnering with Cloudera to offer a comprehensive infrastructure and management solution, based on the Cisco Unified Computing System (UCS), to support our customers big data initiatives. As a proud sponsor for this event, I would encourage you to join us at one of the following scheduled stops to learn more about our joint solutions for big data:
San Francisco on June 4, 2014 (Registration Link Available Soon)
New York on June 18, 2014 (Registration Link Available Soon)
More Cities to be added
Tags: Big Data, Blade Servers, Cisco UCS, Cisco Unified Computing System, Cloudera, Cloudera Sessions, Hadoop, Rack Servers, UCS
Huge amounts of information are flooding companies every second, which has led to an increased focus on big data and the ability to capture and analyze this sea of information. Enterprises are turning to big data and Apache Hadoop in order to improve business performance and provide a competitive advantage. But to unlock business value from data quickly, easily and cost-effectively, organizations need to find and deploy a truly reliable Hadoop infrastructure that can perform, scale, and be used safely for mission-critical applications.
As more and more Hadoop projects are being deployed to provide actionable results in real-time or near real-time, low latency has become a key factor that influences a company’s Hadoop distribution choice. Thus, performance and scalability should be evaluated closely before choosing a particular Hadoop solution.
The raw performance of a Hadoop platform is critical; it refers to how quickly the platform can ingest, process and analyze information. The MapR Distribution for Hadoop in particular provides world-record performance for MapReduce operations on Hadoop. Its advanced architecture harnesses distributed metadata with an optimized shuffle process, delivering consistent high performance.
The graph below compares the MapR M7 Edition with another Hadoop distribution, and it vividly illustrates the vast difference in latency and performance between these Hadoop distributions.
One particular solution that is optimized for performance is Cisco UCS with MapR. MapR on the Cisco Unified Computing System™ (Cisco UCS®) is a powerful, production-ready Hadoop solution that increases business and IT agility, supports mission-critical workloads, reduces total cost of ownership (TCO), and delivers exceptional return on investment (ROI) at scale.
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Tags: Big Data, blade server, Blade Servers, Cisco UCS, Cisco UCS C240 M3 Rack Server, Cisco Unified Computing System, Cisco Unified Data Center, Cisco Unified Fabric, Hadoop, MapR, rack server, UCS Central, UCS service profiles
This week we’re announcing new systems at the upper end of the UCS server product line: some heavy-duty iron for heavy-duty times. These are important new tools for our UCS customers: the digital age is accelerating, IT needs more horsepower to keep up, and there is a lot at stake.
Consider this: less than 10 years ago, some of the largest mainframes scaled up to half a terabyte (TB) of main memory. What if I were to tell you that these latest generation UCS blade servers will scale to 3TB? Sound like a lot? It is. And that’s just the two-processor version. Connect two UCS B260 M4 blades with an expansion connector and they become a UCS B460 M4, a four socket server that will scale to 6TB. Putting that into perspective: a spiffy new laptop might ship today with 8GB of memory. Multiply that by 750 and you have 6TB.
Not too long ago, all the content Wikipedia would fit in this type of footprint (in 2010 it was just under 6TB with media.) Here is a fun illustration of what this scale of data would look like on paper (just the ~10GB of text, not the images.) Now remember, we’re not talking about fitting all that data on the local disks of the server – we’re talking about fitting it in main memory. This is becoming crucially important in the field of data analytics, where “in-memory” is the key to speed and competitiveness. Applications like SAP HANA are at the forefront of this trend. Today, at Intel’s launch event in San Francisco, Dan Morales (Vice President of Enabling Functions at eBay) joined us to talk about how they’re betting on this type of analytic technology to help them make the eBay Marketplace work better for buyers and sellers (and eBay shareholders.) I’ll post a video clip of that soon; his description of the challenges and opportunities, at eBay scale, is worth a watch.
We’ve talked about memory scaling, and Bruno Messina has a nice post that talks more about the scalability on these systems and UCS at large. But dominating performance is the name of the game: behemoth processing performance is what we look for at this end of the server spectrum and Intel has not disappointed on this round of new technology. The next generation of the Intel Xeon E7 family packs up to 15 cores per processor and delivers an average 2x performance increase compared to previous generation products. Performance will be even higher on specific workloads, for example up to 3X on database and even higher for virtualization. Cisco’s implementation of this technology has once again set the standard for system performance. In today’s launch, Intel cited Cisco with 6 industry-leading results on key workloads. As of this posting, the closest to come to that achievement that was Dell with 4. HP ProLiant posted 1. So hats off, once again, to the engineering team in Cisco’s Computing Systems Product Group. Girish Kulkarni has a great summary of the performance news here.
Our collaboration with Intel is one of the best technology combinations in the industry today. Consider what we both bring to the party. Intel: innovation in processor technology that drives Moore’s Law. Cisco: innovation in connecting things across the data center and around the world. UCS is an outcome of two blue-chip tech powerhouses investing in real innovation and the results have changed the industry.
In 1991, Stewart Alsop famously wrote: “I predict that the last mainframe will be unplugged on 15 March 1996.” He just as famously had to eat his words. He munched on those twelve years ago, and while mainframes and RISC-based systems remain, there is an inexorable trend as the heaviest analytic workloads continue to shift to the type of scale-up x86-based systems we’re talking about today. It only makes sense. So while this will garner me plenty of comments from the architectural purists out there, I say “go ahead and plug a mainframe back in.” It will fit right in your UCS B-Series blade chassis…
Tags: Big Data, Blade Servers, Cisco Data Center, Cisco Data Center strategy, Cisco Servers, Cisco UCS, Cisco Unified Computing System, SAP. HANA, unified computing