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Big Data – Hadoop from an Infrastructure Perspective

It’s amazing how some concepts take off like gangbusters in a short duration of time. Big Data is one such concept, that creeps into our conversations because of all the market noise. There is definitely merit to the fundamental premise behind Big Data for most businesses; create better end-user experience, make intelligent business decisions, reduce intellectual waste and monetize on new opportunities or opportunities that did not present itself before. Thus the demand for Data Scientists, application developers, statisticians, mathematicians, etc. -- note these are mostly on the development and analytic side of the house. What’s amazing is large databases have been there for the longest time, in many cases, even the data that are targets now for Big Data applications were also available for the longest time. What has evolved rapidly are the applications tools that facilitate optimized manipulation of massive data sets and flexible interfaces to diverse databases -- example Hadoop.

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Beyond Big Data : Mastering Data In Motion For Positive Business Impact

Last week, more than 8,000 senior business and IT strategists, including more than 2,000 CIOs gathered at the prestigious Gartner Symposium/ITxpo in Orlando, Florida.  At the conference, I presented our vision of how Data in Motion will change the way about we collect, manage and extract value out of data.

The Internet of Everything

Over the last 20 years, the Internet has evolved from digitizing access to information and business processes to digitizing interactions.

The next phase will create connections between all the smart objects around us through a multitude of new sensors connected to the Internet.  Two examples:
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Introducing Cisco UCS Common Platform Architecture (CPA) for Big Data

Updated: 10/01/2013

You may have heard that the digital universe is in petabytes, global IP traffic is in 100s of exabytes. These are mind bogglingly large metrics. Big data analytics can play a crucial role in making datasets in this space usable – by improving operational efficiency to customer experience to prediction accuracy. While Cisco is the global leader in networking -- Did you know that 85% of estimated 500 exabyte global IP traffic in 2012 will pass through Cisco devices ? – the company also builds an innovative family of unified computing products. This enables the company to provide a complete infrastructure solution including compute, storage, connectivity and unified management for big data applications that reduce complexity, improves agility, and radically improves cost of ownership.

To meet a variety of big data platform demands (Hadoop, NoSQL Databases, Massively Parallel Processing Databases etc), Cisco offers a comprehensive solution stack: the Cisco UCS Common Platform Architecture (CPA) for Big Data includes compute, storage, connectivity and unified management. Unique to this architecture is the seamless data integration and management integration capabilities with enterprise application ecosystem including Oracle RDBMS/RAC, Microsoft SQL Server, SAP and others. See Figure 1.

Figure 1:
UCS CPA

The Cisco UCS CPA for Big Data is built using the following components:

  • Cisco UCS 6200 Series Fabric Interconnects provides high speed, low latency connectivity for servers and centralized management for all connected devices with UCS Manager. Deployed in redundant pairs offers the full redundancy, performance (active-active), and exceptional scalability for large number of nodes typical in big data clusters. UCS Manger enables rapid and consistent server integration using service profile, ongoing system maintenance activities such as firmware update operations across the entire cluster as a single operation, advanced monitoring, and option to raise alarms and send notifications about the health of the entire cluster.
  • Cisco UCS 2200 Series Fabric Extenders, act as remote line cards for Fabric Interconnects providing a highly scalable and extremely cost-effective connectivity for large number of nodes.
  • Cisco UCS C240 M3 Rack-Mount Servers, 2-RU server designed for wide range of compute, IO and storage capacity demands. Powered by two Intel Xeon E5-2600 series processors and support up to 768 GB of main memory (typically 128GB or 256GB for big data applications) and up to 24 SFF disk drives in the performance optimized option or 12 LFF disk drives in the capacity optimized option. Also features Cisco UCS VNIC optimized for high bandwidth and low latency cluster connectivity with support for up to 256 virtual devices.
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One More Step Closer “Towards an Industry Standard for Benchmarking Big Data Workloads”

Following the successful workshop “Towards an Industry Standard for Benchmarking Big Data Workloads” (WBDB 2012) held in May 2012 in San Jose [2],  the Second Workshop on Benchmarking Big Data Workloads (WBDB2012.in) [1] will be held in Pune, India from 17 to 18 December at the Hinjewadi Campus of Persistent Systems Ltd, colocated with the 18th International Conference on Management of Data (COMAD 2012) [3].

I have the great pleasure to co-chair this workshop with my distinguished colleagues Chaitanya Baru, Meikel Poess, Milind Bhandarkar and Tilmann Rabl with support from the National Science Foundation (NSF.gov).

The objective of the workshop series is to foster the development of industry standards for providing objective measures of the effectiveness of hardware and software systems dealing with Big Data. Several industry experts and researchers are expected to present and debate their vision on benchmarking big data platforms.

[1] WBDB 2012.in http://clds.ucsd.edu/wbdb2012.in, CFP: http://clds.ucsd.edu/sites/clds.ucsd.edu/files/WBDB.in_.cfp_.pdf
[2] WBDB 2012 http://blogs.cisco.com/datacenter/towards-an-industry-standard-for-benchmarking-big-data-workloads/, http://clds.ucsd.edu/wbdb2012/
[3] COMAD 2012 http://comad.in/comad2012
[4] WBDB 2012.in Program Committee http://clds.ucsd.edu/wbdb2012.in/organizers

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What the Supermarket taught me about Big Data

October 25, 2012 at 12:52 pm PST

Author’s Note:  I have no kids.  I have friends with kids, who used to be in diapers.  The kids were in diapers, not the friends.  I’ve changed a few in my day, but not nearly as many as my friends have. And yes this has some sort of relevance to this story…

 

In every trade show or conference there’s someone talking about Big Data.  They talk about algorithms, CPUs, memory, software stacks, cabling, racks, ROI, TCO, nodes, names, federation, centralization, organization until you get “the pitch.”  I’m not really interested in the pitch for why someone’s product is better than the other, I’m more interested in the “What is the Problem that you’re trying to solve?”  This to me gets to the root of Big Data,or  the consolidation of a set of diverse data sources with a multitude of data types for which you’re attempting to determine relationships and patterns amongst it. Phew. Got it?

Me neither, but I like to think in examples and this is where it dawned on me in the grocery store.

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