Click here to close now.

Welcome!

BigDataExpo® Blog Authors: Liz McMillan, Elizabeth White, William Schmarzo, Adrian Bridgwater, Pat Romanski

Blog Feed Post

Why Contextual Data Locality Matters

Big Data is quickly overtaking SDN as a key phrase in today’s networking lingo. And overused already as it may be, it actually has a lot more meaning and definition compared to SDN. Big Data solutions are designed to work on lots of data as the name suggests. Of course they have been around forever, talk to any large bank, credit card company, airline or logistics company and all of them have had applications running on extremely large databases and data sets forever. But this is the new Big Data, the one inspired by Hadoop, MapReduce and friends. High performance compute clusters specifically created to analyze large amounts of data and reduce it to a form and quantity that human brains can use in decision making.

What makes today’s Big Data solutions different than its more traditional large database based applications, beyond the sheer datasets being analyzed, is the distributed nature of the analysis. Big Data solutions are designed to run across 100s or even 1000s of servers, each with multiple CPU cores to chew on the data. Traditional large database applications tend to be more localized with fewer applications and servers accessing the data, allowing for more tightly custom integrated solutions, the likes of which Oracle and friends are experts at.

Big Data Flashback

In the late 80s I started my career working as a network engineer for a high energy physics research institute. Working closely with the folks at CERN in Geneva, these physicists were (at the time, and probably still) masters of creating very large datasets. Every time an experiment was run, Tbytes of data (probably Pbytes by now) were generated by thousands of sensors along the tunnel or ring particles were passed through to collide.

The Big Data solution at the time was primitive, but not all that much different than today. The large datasets were manually broken into manageable pieces, something that would fit on a tape or disk. These datasets were then hand copied onto a compute server or super computer and the analysis application would churn through it to find specific data, correlate events and simply reduce the data to something smaller and meaningful. This would then create a new dataset, which would be combined, chopped up again, and the process repeated itself until they arrived at data that was consumable for humans to create new theories from, or provide a piece of proof of an existing theory.

During that first job, the IT group spend an enormous amount of time moving data around. A lot of it manual: tapes and disks were constantly being copied onto the appropriate compute server. The data had to be local to have any chance of analyzing the data. Between tapes, local disks and the network, the local disks were the only storage with appropriate speed to have a hope of finalizing the data reductions. And even then it would not be unusual to have a rather powerful (for the time) Apollo workstation run for several weeks on a single data set.

Back to the here and now

Forward the clock to now. The above description is really not that different from how Hadoop MapReduce works. Start with a big data set, chop it into pieces, replicate the data, compute on the data close to physical locality of the data. Then send results to Reducers, combine the results, then perhaps repeat again to get to human interpretable results.

As fast as we believe the network is within 10GbE access ports, it is still commonly the most restrictive component in the compute, distributed storage and network trio. Compute power increments have far outpaced network speed increments and even memory speed increments. We have many more cycles available to compute, but have not been able to get the data into these CPUs with the same increments. As a result, storage solutions are becoming increasingly distributed, closer to the compute power that needs it.

It’s a natural thought to have the data close to where it needs to be processed, close enough that the effort of retrieving it does not impact the overall completion of the task that uses that data. If I am writing a research paper that takes several hours to complete, I do not mind having to wait a second here or there for the right web sites to load. I would mind if I had to get into my car and drive to the library to look something up, drive back home to work on my paper, and keep doing that. The relationship between time and effort to get data has to become negligible compared to the time and effort required to complete the task.

Locality and growth

This type of contextual locality is extremely hard to manage in a dynamic and growing environment. How do you make sure that the right data remains contextually close to where it is needed when servers and VMs may not be physically close? They may not be in the same rack for the same application or customer, they may not even be in the same pod or datacenter. Storage is relatively cheap, but replication for closeness can very quickly lead to a data distribution complexity that is unmanageable in environments where its not a single orchestrated big data solution.

To solve this problem you need help from your network. You need to be able to create locality on the fly. Things that are not physically close need to be made virtually close, but with the characteristics of physical locality. And in network terms these are of course measured in the usual staples of latency and bandwidth. This is when you want to articulate relationships between the data and the applications that need that data and create virtual closeness that resembles the physical. This may mean dedicated paths through multiple switches to avoid congestion that will dramatically impact latency. These same paths can provide direct physical connectivity through dynamically engineered optical paths between application and storage, or simply appropriate prioritization of traffic along these paths. Without having to worry explicitly where the application is or where the storage is.

Physics will always stand in the way of what we really want or need, but that does not mean we use that same physics with a bit of math to create solutions that manage the complexity of creating dynamic locality. Locality is important. More pronounced in Big Data solutions, but even at a smaller scale it is important within the context of the compute effort on that data.

[Today's fun fact: Lake Superior is the world's largest lake. With that kind of naming accuracy we would like to hire the person that named the lake as our VP of Naming and Terminology]

The post Why Contextual Data Locality Matters appeared first on Plexxi.

Read the original blog entry...

More Stories By Michael Bushong

The best marketing efforts leverage deep technology understanding with a highly-approachable means of communicating. Plexxi's Vice President of Marketing Michael Bushong has acquired these skills having spent 12 years at Juniper Networks where he led product management, product strategy and product marketing organizations for Juniper's flagship operating system, Junos. Michael spent the last several years at Juniper leading their SDN efforts across both service provider and enterprise markets. Prior to Juniper, Michael spent time at database supplier Sybase, and ASIC design tool companies Synopsis and Magma Design Automation. Michael's undergraduate work at the University of California Berkeley in advanced fluid mechanics and heat transfer lend new meaning to the marketing phrase "This isn't rocket science."

@BigDataExpo Stories
The web app is Agile. The REST API is Agile. The testing and planning are Agile. But alas, Data infrastructures certainly are not. Once an application matures, changing the shape or indexing scheme of data often forces at best a top down planning exercise and at worst includes schema changes which force downtime. The time has come for a new approach that fundamentally advances the agility of distributed data infrastructures. Come learn about a new solution to the problems faced by software orga...
An effective way of thinking in Big Data is composed of a methodical framework for dealing with the predicted shortage of 50-60% of the qualified Big Data resources in the U.S. This holistic model comprises the scientific and engineering steps that are involved in accelerating Big Data solutions: problem, diagnosis, facts, analysis, hypothesis, solution, prototype and implementation. In his session at Big Data Expo®, Tony Shan focused on the concept, importance, and considerations for each of t...
With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo in Silicon Valley. Learn what is going on, contribute to the discussions, and ensure that your enterprise is as "IoT-Ready" as it can be! Internet of @ThingsExpo, taking place Nov 3-5, 2015, at the Santa Clara Convention Center in Santa Clara, CA, is co-located with 17th Cloud Expo and will feature technical sessions from a rock star conference faculty and the leading in...
SYS-CON Events announced today that BMC will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. BMC delivers software solutions that help IT transform digital enterprises for the ultimate competitive business advantage. BMC has worked with thousands of leading companies to create and deliver powerful IT management services. From mainframe to cloud to mobile, BMC pairs high-speed digital innovation with robust...
Can the spatial component of your Big Data be harnessed and visualized, adding another dimension of power and analytics to your data? In his session at Big Data Expo®, John Meza, Product Engineer and Performance Engineering Team Lead at Esri, discussed the spatial queries that can be used within the Hadoop ecosystem and their integration with GeoSpatial applications. The GIS Tools for Hadoop project was also discussed and its implementation to discover location-based patterns and relationships...
Cloud and Big Data present unique dilemmas: embracing the benefits of these new technologies while maintaining the security of your organization's assets. When an outside party owns, controls and manages your infrastructure and computational resources, how can you be assured that sensitive data remains private and secure? How do you best protect data in mixed use cloud and big data infrastructure sets? Can you still satisfy the full range of reporting, compliance and regulatory requirements? In...
Storage administrators find themselves walking a line between meeting employees’ demands to use public cloud storage services, and their organizations’ need to store information on-premises for security, performance, cost and compliance reasons. However, as file sharing protocols like CIFS and NFS continue to lose their relevance, simply relying only on a NAS-based environment creates inefficiencies that hurt productivity and the bottom line. IT wants to implement cloud storage it can purchase a...
The emergence of cloud computing and Big Data warrants a greater role for the PMO to successfully manage enterprise transformation driven by these powerful trends. As the adoption of cloud-based services continues to grow, a governance model is needed to orchestrate enterprise cloud implementations and harness the power of Big Data analytics. In his session at Cloud Expo, Mahesh Singh, President of BigData, Inc., discussed how the Enterprise PMO takes center stage not only in developing the app...
In their session at @ThingsExpo, Shyam Varan Nath, Principal Architect at GE, and Ibrahim Gokcen, who leads GE's advanced IoT analytics, focused on the Internet of Things / Industrial Internet and how to make it operational for business end-users. Learn about the challenges posed by machine and sensor data and how to marry it with enterprise data. They also discussed the tips and tricks to provide the Industrial Internet as an end-user consumable service using Big Data Analytics and Industrial C...
Are your Big Data initiatives resulting in Big Impact or Big Mess? In her session at Big Data Expo, Penelope Everall Gordon, Emerging Technology Strategist at 1Plug Corporation, shared her successes in improving Big Decision outcomes by building stories compelling to the target audience – and her failures when she lost sight of the plotline, distracted by the glitter of technology and the lure of buried insights. The cast of characters includes the agency head [city official? elected official?...
We certainly live in interesting technological times. And no more interesting than the current competing IoT standards for connectivity. Various standards bodies, approaches, and ecosystems are vying for mindshare and positioning for a competitive edge. It is clear that when the dust settles, we will have new protocols, evolved protocols, that will change the way we interact with devices and infrastructure. We will also have evolved web protocols, like HTTP/2, that will be changing the very core...
How do APIs and IoT relate? The answer is not as simple as merely adding an API on top of a dumb device, but rather about understanding the architectural patterns for implementing an IoT fabric. There are typically two or three trends: Exposing the device to a management framework Exposing that management framework to a business centric logic Exposing that business layer and data to end users. This last trend is the IoT stack, which involves a new shift in the separation of what stuff happe...
Move from reactive to proactive cloud management in a heterogeneous cloud infrastructure. In his session at 16th Cloud Expo, Manoj Khabe, Innovative Solution-Focused Transformation Leader at Vicom Computer Services, Inc., will show how to replace a help desk-centric approach with an ITIL-based service model and service-centric CMDB that’s tightly integrated with an event and incident management platform. Learn how to expand the scope of operations management to service management. He will al...
The true value of the Internet of Things (IoT) lies not just in the data, but through the services that protect the data, perform the analysis and present findings in a usable way. With many IoT elements rooted in traditional IT components, Big Data and IoT isn’t just a play for enterprise. In fact, the IoT presents SMBs with the prospect of launching entirely new activities and exploring innovative areas. CompTIA research identifies several areas where IoT is expected to have the greatest impac...
Amazon and Google have built software-defined data centers (SDDCs) that deliver massively scalable services with great efficiency. Yet, building SDDCs has proven to be a near impossibility for companies without hyper-scale resources. In his session at 15th Cloud Expo, David Cauthron, CTO and Founder of NIMBOXX, highlighted how a mid-sized manufacturer of global industrial equipment bridged the gap from virtualization to software-defined services, streamlining operations and costs while connect...
The Industrial Internet revolution is now underway, enabled by connected machines and billions of devices that communicate and collaborate. The massive amounts of Big Data requiring real-time analysis is flooding legacy IT systems and giving way to cloud environments that can handle the unpredictable workloads. Yet many barriers remain until we can fully realize the opportunities and benefits from the convergence of machines and devices with Big Data and the cloud, including interoperability, ...
Discussions about cloud computing are evolving into discussions about enterprise IT in general. As enterprises increasingly migrate toward their own unique clouds, new issues such as the use of containers and microservices emerge to keep things interesting. In this Power Panel at 16th Cloud Expo, moderated by Conference Chair Roger Strukhoff, panelists will address the state of cloud computing today, and what enterprise IT professionals need to know about how the latest topics and trends affec...
While there are hundreds of public and private cloud hosting providers to choose from, not all clouds are created equal. If you’re seeking to host enterprise-level mission-critical applications, where Cloud Security is a primary concern, WHOA.com is setting new standards for cloud hosting, and has established itself as a major contender in the marketplace. We are constantly seeking ways to innovate and leverage state-of-the-art technologies. In his session at 16th Cloud Expo, Mike Rivera, Seni...
The Internet of Things is tied together with a thin strand that is known as time. Coincidentally, at the core of nearly all data analytics is a timestamp. When working with time series data there are a few core principles that everyone should consider, especially across datasets where time is the common boundary. In his session at Internet of @ThingsExpo, Jim Scott, Director of Enterprise Strategy & Architecture at MapR Technologies, discussed single-value, geo-spatial, and log time series dat...
All major researchers estimate there will be tens of billions devices - computers, smartphones, tablets, and sensors - connected to the Internet by 2020. This number will continue to grow at a rapid pace for the next several decades. With major technology companies and startups seriously embracing IoT strategies, now is the perfect time to attend @ThingsExpo, June 9-11, 2015, at the Javits Center in New York City. Learn what is going on, contribute to the discussions, and ensure that your enter...