|By Michael Bushong||
|August 21, 2014 06:00 AM EDT||
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]
VictorOps is making on-call suck less with the only collaborative alert management platform on the market. With easy on-call scheduling management, a real-time incident timeline that gives you contextual relevance around your alerts and powerful reporting features that make post-mortems more effective, VictorOps helps your IT/DevOps team solve problems faster.
Mar. 1, 2015 05:00 PM EST Reads: 1,237
Skeuomorphism usually means retaining existing design cues in something new that doesn’t actually need them. However, the concept of skeuomorphism can be thought of as relating more broadly to applying existing patterns to new technologies that, in fact, cry out for new approaches. In his session at DevOps Summit, Gordon Haff, Senior Cloud Strategy Marketing and Evangelism Manager at Red Hat, will discuss why containers should be paired with new architectural practices such as microservices ra...
Mar. 1, 2015 04:00 PM EST Reads: 1,458
Roberto Medrano, Executive Vice President at SOA Software, had reached 30,000 page views on his home page - http://RobertoMedrano.SYS-CON.com/ - on the SYS-CON family of online magazines, which includes Cloud Computing Journal, Internet of Things Journal, Big Data Journal, and SOA World Magazine. He is a recognized executive in the information technology fields of SOA, internet security, governance, and compliance. He has extensive experience with both start-ups and large companies, having been ...
Mar. 1, 2015 04:00 PM EST Reads: 1,264
The industrial software market has treated data with the mentality of “collect everything now, worry about how to use it later.” We now find ourselves buried in data, with the pervasive connectivity of the (Industrial) Internet of Things only piling on more numbers. There’s too much data and not enough information. In his session at @ThingsExpo, Bob Gates, Global Marketing Director, GE’s Intelligent Platforms business, to discuss how realizing the power of IoT, software developers are now focu...
Mar. 1, 2015 03:15 PM EST Reads: 1,364
Operational Hadoop and the Lambda Architecture for Streaming Data Apache Hadoop is emerging as a distributed platform for handling large and fast incoming streams of data. Predictive maintenance, supply chain optimization, and Internet-of-Things analysis are examples where Hadoop provides the scalable storage, processing, and analytics platform to gain meaningful insights from granular data that is typically only valuable from a large-scale, aggregate view. One architecture useful for capturing...
Mar. 1, 2015 02:00 PM EST Reads: 1,352
SYS-CON Events announced today that Vitria Technology, Inc. will exhibit at SYS-CON’s @ThingsExpo, which will take place on June 9-11, 2015, at the Javits Center in New York City, NY. Vitria will showcase the company’s new IoT Analytics Platform through live demonstrations at booth #330. Vitria’s IoT Analytics Platform, fully integrated and powered by an operational intelligence engine, enables customers to rapidly build and operationalize advanced analytics to deliver timely business outcomes ...
Mar. 1, 2015 01:45 PM EST Reads: 1,230
DevOps is about increasing efficiency, but nothing is more inefficient than building the same application twice. However, this is a routine occurrence with enterprise applications that need both a rich desktop web interface and strong mobile support. With recent technological advances from Isomorphic Software and others, it is now feasible to create a rich desktop and tuned mobile experience with a single codebase, without compromising performance or usability.
Mar. 1, 2015 01:15 PM EST Reads: 1,131
SYS-CON Events announced today Arista Networks will exhibit at SYS-CON's DevOps Summit 2015 New York, which will take place June 9-11, 2015, at the Javits Center in New York City, NY. Arista Networks was founded to deliver software-driven cloud networking solutions for large data center and computing environments. Arista’s award-winning 10/40/100GbE switches redefine scalability, robustness, and price-performance, with over 3,000 customers and more than three million cloud networking ports depl...
Mar. 1, 2015 01:00 PM EST Reads: 1,566
The speed of software changes in growing and large scale rapid-paced DevOps environments presents a challenge for continuous testing. Many organizations struggle to get this right. Practices that work for small scale continuous testing may not be sufficient as the requirements grow. In his session at DevOps Summit, Marc Hornbeek, Sr. Solutions Architect of DevOps continuous test solutions at Spirent Communications, will explain the best practices of continuous testing at high scale, which is r...
Mar. 1, 2015 01:00 PM EST Reads: 1,205
SYS-CON Events announced today that Open Data Centers (ODC), a carrier-neutral colocation provider, will exhibit at SYS-CON's 16th International Cloud Expo®, which will take place June 9-11, 2015, at the Javits Center in New York City, NY. Open Data Centers is a carrier-neutral data center operator in New Jersey and New York City offering alternative connectivity options for carriers, service providers and enterprise customers.
Mar. 1, 2015 12:00 PM EST Reads: 1,927
Thanks to Docker, it becomes very easy to leverage containers to build, ship, and run any Linux application on any kind of infrastructure. Docker is particularly helpful for microservice architectures because their successful implementation relies on a fast, efficient deployment mechanism – which is precisely one of the features of Docker. Microservice architectures are therefore becoming more popular, and are increasingly seen as an interesting option even for smaller projects, instead of bein...
Mar. 1, 2015 12:00 PM EST Reads: 2,574
Security can create serious friction for DevOps processes. We've come up with an approach to alleviate the friction and provide security value to DevOps teams. In her session at DevOps Summit, Shannon Lietz, Senior Manager of DevSecOps at Intuit, will discuss how DevSecOps got started and how it has evolved. Shannon Lietz has over two decades of experience pursuing next generation security solutions. She is currently the DevSecOps Leader for Intuit where she is responsible for setting and driv...
Mar. 1, 2015 12:00 PM EST Reads: 2,376
The explosion of connected devices / sensors is creating an ever-expanding set of new and valuable data. In parallel the emerging capability of Big Data technologies to store, access, analyze, and react to this data is producing changes in business models under the umbrella of the Internet of Things (IoT). In particular within the Insurance industry, IoT appears positioned to enable deep changes by altering relationships between insurers, distributors, and the insured. In his session at @Things...
Mar. 1, 2015 12:00 PM EST Reads: 1,278
Even as cloud and managed services grow increasingly central to business strategy and performance, challenges remain. The biggest sticking point for companies seeking to capitalize on the cloud is data security. Keeping data safe is an issue in any computing environment, and it has been a focus since the earliest days of the cloud revolution. Understandably so: a lot can go wrong when you allow valuable information to live outside the firewall. Recent revelations about government snooping, along...
Mar. 1, 2015 11:00 AM EST Reads: 6,968
In his session at DevOps Summit, Tapabrata Pal, Director of Enterprise Architecture at Capital One, will tell a story about how Capital One has embraced Agile and DevOps Security practices across the Enterprise – driven by Enterprise Architecture; bringing in Development, Operations and Information Security organizations together. Capital Ones DevOpsSec practice is based upon three "pillars" – Shift-Left, Automate Everything, Dashboard Everything. Within about three years, from 100% waterfall, C...
Mar. 1, 2015 11:00 AM EST Reads: 2,728
PubNub on Monday has announced that it is partnering with IBM to bring its sophisticated real-time data streaming and messaging capabilities to Bluemix, IBM’s cloud development platform. “Today’s app and connected devices require an always-on connection, but building a secure, scalable solution from the ground up is time consuming, resource intensive, and error-prone,” said Todd Greene, CEO of PubNub. “PubNub enables web, mobile and IoT developers building apps on IBM Bluemix to quickly add sc...
Mar. 1, 2015 10:00 AM EST Reads: 4,771
Data-intensive companies that strive to gain insights from data using Big Data analytics tools can gain tremendous competitive advantage by deploying data-centric storage. Organizations generate large volumes of data, the vast majority of which is unstructured. As the volume and velocity of this unstructured data increases, the costs, risks and usability challenges associated with managing the unstructured data (regardless of file type, size or device) increases simultaneously, including end-to-...
Mar. 1, 2015 09:45 AM EST Reads: 2,219
The excitement around the possibilities enabled by Big Data is being tempered by the daunting task of feeding the analytics engines with high quality data on a continuous basis. As the once distinct fields of data integration and data management increasingly converge, cloud-based data solutions providers have emerged that can buffer your organization from the complexities of this continuous data cleansing and management so that you’re free to focus on the end goal: actionable insight.
Mar. 1, 2015 09:30 AM EST Reads: 1,675
Between the compelling mockups and specs produced by your analysts and designers, and the resulting application built by your developers, there is a gulf where projects fail, costs spiral out of control, and applications fall short of requirements. In his session at DevOps Summit, Charles Kendrick, CTO and Chief Architect at Isomorphic Software, will present a new approach where business and development users collaborate – each using tools appropriate to their goals and expertise – to build mo...
Mar. 1, 2015 09:00 AM EST Reads: 2,917
The Internet of Things (IoT) is causing data centers to become radically decentralized and atomized within a new paradigm known as “fog computing.” To support IoT applications, such as connected cars and smart grids, data centers' core functions will be decentralized out to the network's edges and endpoints (aka “fogs”). As this trend takes hold, Big Data analytics platforms will focus on high-volume log analysis (aka “logs”) and rely heavily on cognitive-computing algorithms (aka “cogs”) to mak...
Mar. 1, 2015 09:00 AM EST Reads: 1,067