|By Jack Norris||
|August 19, 2012 08:15 AM EDT||
We have entered the "Age of Big Data" according to a recent New York Times article. This comes as no surprise to most organizations already struggling with the onslaught of data coming from an increasing number of sources and at an increasing rate. The 2011 IDC Digital Universe Study reported that data is growing faster than Moore's Law. This trend points to a paradigm shift in how organizations process data where isolated islands and silos are being replaced by large clusters of commodity servers that keep data and compute resources together.
Another way of looking at this paradigm shift is that the growing volume and velocity of data require a new approach to networked computing. A good example of this change is found at Google. The industry now takes Google's dominance for granted, but when Google launched its beta search engine in 1998, the company was late entering the market. At the time, Yahoo! was dominant; other contenders included infoseek, excite, Lycos, Ask Jeeves and AltaVista (dominating technical searches). Within two years, Google was the dominant search provider. It wasn't until 2003, when Google published a paper on MapReduce, that the world got a glimpse into Google's back-end architecture.
Google's architecture revealed how the company was able to index significantly more data, to get far better results faster, and to achieve these superior results much more efficiently and cost-effectively than all competitors. The shift Google made was to divide complex data analysis tasks into simple subtasks that could be performed in parallel on commodity servers. Separate processes were being used to Map the data, and then Reduce it into interim or final results. This MapReduce framework would eventually become available to organizations through distributions of Apache Hadoop.
A Brief History of Hadoop
After reading Google's paper in 2003, Yahoo engineer Doug Cutting developed a Java-based implementation of MapReduce, and named it after his son's stuffed elephant, Hadoop. In 2006, Hadoop became a subproject of Lucene (a popular text search library) at the Apache Software Foundation (www.apache.org), and became its own top-level Apache project in 2008.
Essentially, Hadoop provides a way to capture, organize, store, search, share, analyze and visualize disparate data sources (structured, semi-structured and unstructured) across a large cluster of commodity computers, and is designed to scale up from dozens to thousands of servers, each offering local computation and storage.
While there are several elements that are now part of Hadoop, two are fundamental to its operation. The first is the Hadoop Distributed File System (HDFS), which serves as the primary storage system. HDFS replicates and distributes the blocks of source data to the compute nodes throughout the cluster of servers to be analyzed by one or more applications. The second is MapReduce, which creates a software framework and a programming model for writing applications capable of processing vast amounts of distributed data in parallel on very large clusters.
The open source nature of Apache Hadoop creates an ecosystem that facilitates constant advancements in its capabilities, performance, reliability and ease of use. These enhancements can be made by any individual or organization-a global community of contributors-and are then either contributed to the basic Apache library or made available in a separate (often free) commercial distribution.
In effect, Hadoop is a complete system or "stack" for data analysis. The stack includes not only the HDFS and MapReduce foundation, but also job management, development tools, schedulers, machine learning libraries, etc.
KISS: Keep It Simple, Scalable
In a paper titled The Unreasonable Effectiveness of Data, the authors (all research directors from Google) make a contrast between the elegant simplicity of physics (with equations like E = mc2) and other disciplines, noting that, "... sciences that involve human beings rather than elementary particles have proven more resistant to elegant mathematics."
The fact that simple formulas are fully capable of explaining the complex natural world, while remaining elusive in understanding human behavior, is fundamental to why Hadoop is gaining in popularity. The paper notes the frustration of economists, who lack similar simple equations or models, and explores advances being made in fields like natural language processing-a notoriously complex area that has been studied for years with many attempts at artificial intelligence as a means to gain some insight.
The authors found that relatively simple algorithms applied to massive datasets produced stunning results. One example involves scene completion. An algorithm was used to eliminate something in a picture, a car for instance, and then based on a corpus of thousands of pictures, fill in the missing background. The algorithm performed rather poorly until the corpus was increased to millions of photos. With sufficient data, the same, simple algorithm performed extremely well. This need to find patterns and fill in the "missing pieces" in any puzzle is a common theme in many data analytics applications today.
Data analytics also confronts another inherent complexity: the growth in unstructured and semi-structured data. The sources of unstructured data, such as log files, social media, videos, etc., are growing in both their size and importance. But even structured data that goes through a series of changes eventually loses some or all of its structure. Traditional analytic techniques require considerable preprocessing of unstructured and semi-structured data before being able to produce results, and the results can be wrong or misleading if the preprocessing is somehow flawed.
The ability of Hadoop to employ simple algorithms and obtain meaningful results when analyzing unstructured, semi-structured and structured data in its raw form is unprecedented-and currently unparalleled. MapReduce enables data to be analyzed in an incremental fashion (and with parallel processing) without any need to engage in complex data transformations or to otherwise preprocess any data sources, or to create any schemas or aggregate any data in advance. Sometimes the interim results can be quite revealing on their own, and any unexpected results can be used to further fine-tune additional analysis. In fact, Hadoop was designed to accommodate virtually all forms of data directly, thus eliminating the need to engage in extraordinary measures before being able to unlock the value hidden deeply within.
The Price/Performance of Data Analytics
Not only does Hadoop deliver superior data analytics capabilities and results, it does so (as Google found) with an infrastructure that is far more cost-effective than traditional data analysis tools. The reason is that scaling data analytics capabilities has long been subject to the 80/20 rule: Big gains can be achieved with little initial effort (and cost), but the returns diminish as the datasets grow to become Big Data.
In stark contrast, Hadoop can scale linearly, which is the key to both effective and cost-effective data analytics. As datasets grow, traditional data analysis environments scale in an exponential fashion, causing the additional cost required to gain additional insight to eventually become prohibitive. With Hadoop, by contrast, the cluster of commodity (read: inexpensive) servers with direct-attached storage scales linearly with the growth in the number and sizes of datasets.
Hadoop's ability to satisfy these prerequisites well is the reason for its growing popularity in Web-based businesses and data-intensive organizations, as well as at aggressive start-ups. For the former, the need to wrestle with truly Big Data justifies the need for a data analytics environment like Hadoop. For the latter, the lack of anything legacy makes it easy to benefit from Hadoop's advantages.
One major challenge to Hadoop adoption, however, remains its file system. HDFS is an append-only storage that requires data to be batch loaded in a Hadoop cluster and then later exported post-processing for use by other applications that don't support the HDFS API. And Big Data can be difficult and costly to move back and forth in this fashion owing to the inherent duplication of data across the "semantic wall" between the existing and Hadoop infrastructures.
Another barrier to production adoption of Hadoop in larger organizations involves the extraordinary measures required to make the environment dependable. Constant care is needed to ensure that single points of failure (especially in the NameNode and JobTracker) cannot cause catastrophe, and that in the case of data loss, data can be re-loaded into the Hadoop cluster.
Breaking Through the Barriers
These problems with Hadoop are, themselves, becoming part of the past. Open source communities can be quite large, creating a vibrant ecosystem. This is the case with Hadoop, where several companies are now providing commercial distributions based on open source Hadoop.
The growing number of commercial Hadoop distributions available is systematically breaking through the barriers to widespread adoption. In general, these distributions provide enhancements that make Hadoop easier to integrate into the enterprise, as well as more enterprise-class in its operation, performance and reliability. One way of achieving these enhancements is to use existing and standard communications protocols as a foundation to enable more seamless integration between legacy and Hadoop environments.
Such a common foundation facilitates making the paradigm shift in data analytics in virtually any organization. It eliminates the need to throw data back and forth over a "semantic wall" by tearing down that wall. The compatibility afforded also extends beyond the physical infrastructure and into development environments and routine operating procedures, especially those involving data protection, such as snapshots and mirroring. With standards-based file access into the Hadoop cluster, existing applications and tools, and even ordinary browsers are able to access the data directly and in real-time (vs. Hadoop's traditional batch processing.
The End - or Just the Beginning
The data analytics paradigm is changing, and the change presents a real opportunity for established organizations to take full advantage of some new and powerful capabilities without sacrificing any existing ones. Just as Google was able to do, Hadoop makes it possible for any organization to gain a significant competitive edge by taking full advantage of the insight provided by this paradigm shift.
Hadoop is indeed a game-changing technology, and Hadoop is now itself changing with the advent of enterprise-class commercial distributions. By making Hadoop more mission-critical in its operation (potentially with the same or an even lower total cost of ownership), these "next-generation" solutions make beginning the shift to the new data analytics paradigm less risky and more rewarding than ever before.
Connected devices and the Internet of Things are getting significant momentum in 2014. In his session at Internet of @ThingsExpo, Jim Hunter, Chief Scientist & Technology Evangelist at Greenwave Systems, examined three key elements that together will drive mass adoption of the IoT before the end of 2015. The first element is the recent advent of robust open source protocols (like AllJoyn and WebRTC) that facilitate M2M communication. The second is broad availability of flexible, cost-effective storage designed to handle the massive surge in back-end data in a world where timely analytics is e...
Nov. 26, 2014 10:45 PM EST Reads: 966
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 happens, where data lives and where the interface lies. For instance, it's a mix of architectural styles ...
Nov. 26, 2014 10:30 PM EST Reads: 816
The Internet of Things will put IT to its ultimate test by creating infinite new opportunities to digitize products and services, generate and analyze new data to improve customer satisfaction, and discover new ways to gain a competitive advantage across nearly every industry. In order to help corporate business units to capitalize on the rapidly evolving IoT opportunities, IT must stand up to a new set of challenges. In his session at @ThingsExpo, Jeff Kaplan, Managing Director of THINKstrategies, will examine why IT must finally fulfill its role in support of its SBUs or face a new round of...
Nov. 26, 2014 09:00 PM EST Reads: 933
We are reaching the end of the beginning with WebRTC, and real systems using this technology have begun to appear. One challenge that faces every WebRTC deployment (in some form or another) is identity management. For example, if you have an existing service – possibly built on a variety of different PaaS/SaaS offerings – and you want to add real-time communications you are faced with a challenge relating to user management, authentication, authorization, and validation. Service providers will want to use their existing identities, but these will have credentials already that are (hopefully) i...
Nov. 26, 2014 07:00 PM EST Reads: 960
Cultural, regulatory, environmental, political and economic (CREPE) conditions over the past decade are creating cross-industry solution spaces that require processes and technologies from both the Internet of Things (IoT), and Data Management and Analytics (DMA). These solution spaces are evolving into Sensor Analytics Ecosystems (SAE) that represent significant new opportunities for organizations of all types. Public Utilities throughout the world, providing electricity, natural gas and water, are pursuing SmartGrid initiatives that represent one of the more mature examples of SAE. We have s...
Nov. 26, 2014 06:00 PM EST Reads: 966
"Matrix is an ambitious open standard and implementation that's set up to break down the fragmentation problems that exist in IP messaging and VoIP communication," explained John Woolf, Technical Evangelist at Matrix, in this SYS-CON.tv interview at @ThingsExpo, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Nov. 26, 2014 05:45 PM EST Reads: 912
The Internet of Things will greatly expand the opportunities for data collection and new business models driven off of that data. In her session at @ThingsExpo, Esmeralda Swartz, CMO of MetraTech, discussed how for this to be effective you not only need to have infrastructure and operational models capable of utilizing this new phenomenon, but increasingly service providers will need to convince a skeptical public to participate. Get ready to show them the money!
Nov. 26, 2014 04:00 PM EST Reads: 1,004
One of the biggest challenges when developing connected devices is identifying user value and delivering it through successful user experiences. In his session at Internet of @ThingsExpo, Mike Kuniavsky, Principal Scientist, Innovation Services at PARC, described an IoT-specific approach to user experience design that combines approaches from interaction design, industrial design and service design to create experiences that go beyond simple connected gadgets to create lasting, multi-device experiences grounded in people's real needs and desires.
Nov. 26, 2014 03:45 PM EST Reads: 972
P2P RTC will impact the landscape of communications, shifting from traditional telephony style communications models to OTT (Over-The-Top) cloud assisted & PaaS (Platform as a Service) communication services. The P2P shift will impact many areas of our lives, from mobile communication, human interactive web services, RTC and telephony infrastructure, user federation, security and privacy implications, business costs, and scalability. In his session at @ThingsExpo, Robin Raymond, Chief Architect at Hookflash, will walk through the shifting landscape of traditional telephone and voice services ...
Nov. 26, 2014 02:00 PM EST Reads: 1,469
Scott Jenson leads a project called The Physical Web within the Chrome team at Google. Project members are working to take the scalability and openness of the web and use it to talk to the exponentially exploding range of smart devices. Nearly every company today working on the IoT comes up with the same basic solution: use my server and you'll be fine. But if we really believe there will be trillions of these devices, that just can't scale. We need a system that is open a scalable and by using the URL as a basic building block, we open this up and get the same resilience that the web enjoys.
Nov. 25, 2014 09:30 PM EST Reads: 1,223
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 data. By focusing on enterprise applications and the data center, he will use OpenTSDB as an example t...
Nov. 25, 2014 09:30 PM EST Reads: 1,274
The Domain Name Service (DNS) is one of the most important components in networking infrastructure, enabling users and services to access applications by translating URLs (names) into IP addresses (numbers). Because every icon and URL and all embedded content on a website requires a DNS lookup loading complex sites necessitates hundreds of DNS queries. In addition, as more internet-enabled ‘Things' get connected, people will rely on DNS to name and find their fridges, toasters and toilets. According to a recent IDG Research Services Survey this rate of traffic will only grow. What's driving t...
Nov. 25, 2014 07:00 PM EST Reads: 1,313
Enthusiasm for the Internet of Things has reached an all-time high. In 2013 alone, venture capitalists spent more than $1 billion dollars investing in the IoT space. With "smart" appliances and devices, IoT covers wearable smart devices, cloud services to hardware companies. Nest, a Google company, detects temperatures inside homes and automatically adjusts it by tracking its user's habit. These technologies are quickly developing and with it come challenges such as bridging infrastructure gaps, abiding by privacy concerns and making the concept a reality. These challenges can't be addressed w...
Nov. 25, 2014 04:30 PM EST Reads: 1,318
Explosive growth in connected devices. Enormous amounts of data for collection and analysis. Critical use of data for split-second decision making and actionable information. All three are factors in making the Internet of Things a reality. Yet, any one factor would have an IT organization pondering its infrastructure strategy. How should your organization enhance its IT framework to enable an Internet of Things implementation? In his session at Internet of @ThingsExpo, James Kirkland, Chief Architect for the Internet of Things and Intelligent Systems at Red Hat, described how to revolutioniz...
Nov. 24, 2014 07:00 PM EST Reads: 1,625
Bit6 today issued a challenge to the technology community implementing Web Real Time Communication (WebRTC). To leap beyond WebRTC’s significant limitations and fully leverage its underlying value to accelerate innovation, application developers need to consider the entire communications ecosystem.
Nov. 24, 2014 12:00 PM EST Reads: 1,514
The definition of IoT is not new, in fact it’s been around for over a decade. What has changed is the public's awareness that the technology we use on a daily basis has caught up on the vision of an always on, always connected world. If you look into the details of what comprises the IoT, you’ll see that it includes everything from cloud computing, Big Data analytics, “Things,” Web communication, applications, network, storage, etc. It is essentially including everything connected online from hardware to software, or as we like to say, it’s an Internet of many different things. The difference ...
Nov. 24, 2014 11:00 AM EST Reads: 1,652
Cloud Expo 2014 TV commercials will feature @ThingsExpo, which was launched in June, 2014 at New York City's Javits Center as the largest 'Internet of Things' event in the world.
Nov. 24, 2014 09:00 AM EST Reads: 1,665
SYS-CON Events announced today that Windstream, a leading provider of advanced network and cloud communications, has been named “Silver Sponsor” of SYS-CON's 16th International Cloud Expo®, which will take place on June 9–11, 2015, at the Javits Center in New York, NY. Windstream (Nasdaq: WIN), a FORTUNE 500 and S&P 500 company, is a leading provider of advanced network communications, including cloud computing and managed services, to businesses nationwide. The company also offers broadband, phone and digital TV services to consumers primarily in rural areas.
Nov. 23, 2014 07:30 PM EST Reads: 1,836
"There is a natural synchronization between the business models, the IoT is there to support ,” explained Brendan O'Brien, Co-founder and Chief Architect of Aria Systems, in this SYS-CON.tv interview at the 15th International Cloud Expo®, held Nov 4–6, 2014, at the Santa Clara Convention Center in Santa Clara, CA.
Nov. 23, 2014 12:00 PM EST Reads: 1,786
The major cloud platforms defy a simple, side-by-side analysis. Each of the major IaaS public-cloud platforms offers their own unique strengths and functionality. Options for on-site private cloud are diverse as well, and must be designed and deployed while taking existing legacy architecture and infrastructure into account. Then the reality is that most enterprises are embarking on a hybrid cloud strategy and programs. In this Power Panel at 15th Cloud Expo (http://www.CloudComputingExpo.com), moderated by Ashar Baig, Research Director, Cloud, at Gigaom Research, Nate Gordon, Director of T...
Nov. 23, 2014 07:45 AM EST Reads: 1,815