Apache Authors: Pat Romanski, John Mertic, Liz McMillan, Elizabeth White, Janakiram MSV

Related Topics: @CloudExpo, Microservices Expo, Open Source Cloud, Containers Expo Blog, Agile Computing, Apache

@CloudExpo: Article

The Age of Big Data: How to Gain Competitive Advantage

The Drivers Behind Hadoop Adoption

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.

More Stories By Jack Norris

Jack Norris is vice president, marketing, MapR Technologies. He has over 20 years of enterprise software marketing experience. He leads worldwide marketing for the industry’s most advanced distribution for Hadoop. Jack’s experience ranges from defining new markets for small companies, leading marketing and business development for an early-stage cloud storage software provider, to increasing sales of new products for large public companies. Jack has also held senior executive roles with Brio Technology, SQRIBE, EMC, Rainfinity, and Bain and Company.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.

@ThingsExpo Stories
In his general session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed cloud as a ‘better data center’ and how it adds new capacity (faster) and improves application availability (redundancy). The cloud is a ‘Dynamic Tool for Dynamic Apps’ and resource allocation is an integral part of your application architecture, so use only the resources you need and allocate /de-allocate resources on the fly.
We're entering the post-smartphone era, where wearable gadgets from watches and fitness bands to glasses and health aids will power the next technological revolution. With mass adoption of wearable devices comes a new data ecosystem that must be protected. Wearables open new pathways that facilitate the tracking, sharing and storing of consumers’ personal health, location and daily activity data. Consumers have some idea of the data these devices capture, but most don’t realize how revealing and...
A completely new computing platform is on the horizon. They’re called Microservers by some, ARM Servers by others, and sometimes even ARM-based Servers. No matter what you call them, Microservers will have a huge impact on the data center and on server computing in general. Although few people are familiar with Microservers today, their impact will be felt very soon. This is a new category of computing platform that is available today and is predicted to have triple-digit growth rates for some ...
SYS-CON Events announced today that MathFreeOn will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. MathFreeOn is Software as a Service (SaaS) used in Engineering and Math education. Write scripts and solve math problems online. MathFreeOn provides online courses for beginners or amateurs who have difficulties in writing scripts. In accordance with various mathematical topics, there are more tha...
In past @ThingsExpo presentations, Joseph di Paolantonio has explored how various Internet of Things (IoT) and data management and analytics (DMA) solution spaces will come together as sensor analytics ecosystems. This year, in his session at @ThingsExpo, Joseph di Paolantonio from DataArchon, will be adding the numerous Transportation areas, from autonomous vehicles to “Uber for containers.” While IoT data in any one area of Transportation will have a huge impact in that area, combining sensor...
SYS-CON Events announced today that SoftNet Solutions will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. SoftNet Solutions specializes in Enterprise Solutions for Hadoop and Big Data. It offers customers the most open, robust, and value-conscious portfolio of solutions, services, and tools for the shortest route to success with Big Data. The unique differentiator is the ability to architect and ...
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
More and more brands have jumped on the IoT bandwagon. We have an excess of wearables – activity trackers, smartwatches, smart glasses and sneakers, and more that track seemingly endless datapoints. However, most consumers have no idea what “IoT” means. Creating more wearables that track data shouldn't be the aim of brands; delivering meaningful, tangible relevance to their users should be. We're in a period in which the IoT pendulum is still swinging. Initially, it swung toward "smart for smar...
@ThingsExpo has been named the Top 5 Most Influential Internet of Things Brand by Onalytica in the ‘The Internet of Things Landscape 2015: Top 100 Individuals and Brands.' Onalytica analyzed Twitter conversations around the #IoT debate to uncover the most influential brands and individuals driving the conversation. Onalytica captured data from 56,224 users. The PageRank based methodology they use to extract influencers on a particular topic (tweets mentioning #InternetofThings or #IoT in this ...
SYS-CON Events announced today that Niagara Networks will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Niagara Networks offers the highest port-density systems, and the most complete Next-Generation Network Visibility systems including Network Packet Brokers, Bypass Switches, and Network TAPs.
In an era of historic innovation fueled by unprecedented access to data and technology, the low cost and risk of entering new markets has leveled the playing field for business. Today, any ambitious innovator can easily introduce a new application or product that can reinvent business models and transform the client experience. In their Day 2 Keynote at 19th Cloud Expo, Mercer Rowe, IBM Vice President of Strategic Alliances, and Raejeanne Skillern, Intel Vice President of Data Center Group and ...
Data is the fuel that drives the machine learning algorithmic engines and ultimately provides the business value. In his session at Cloud Expo, Ed Featherston, a director and senior enterprise architect at Collaborative Consulting, will discuss the key considerations around quality, volume, timeliness, and pedigree that must be dealt with in order to properly fuel that engine.
Virgil consists of an open-source encryption library, which implements Cryptographic Message Syntax (CMS) and Elliptic Curve Integrated Encryption Scheme (ECIES) (including RSA schema), a Key Management API, and a cloud-based Key Management Service (Virgil Keys). The Virgil Keys Service consists of a public key service and a private key escrow service. 

Fact is, enterprises have significant legacy voice infrastructure that’s costly to replace with pure IP solutions. How can we bring this analog infrastructure into our shiny new cloud applications? There are proven methods to bind both legacy voice applications and traditional PSTN audio into cloud-based applications and services at a carrier scale. Some of the most successful implementations leverage WebRTC, WebSockets, SIP and other open source technologies. In his session at @ThingsExpo, Da...
Fifty billion connected devices and still no winning protocols standards. HTTP, WebSockets, MQTT, and CoAP seem to be leading in the IoT protocol race at the moment but many more protocols are getting introduced on a regular basis. Each protocol has its pros and cons depending on the nature of the communications. Does there really need to be only one protocol to rule them all? Of course not. In his session at @ThingsExpo, Chris Matthieu, co-founder and CTO of Octoblu, walk you through how Oct...
SYS-CON Events announced today that Embotics, the cloud automation company, will exhibit at the 19th International Cloud Expo, which will take place on November 1–3, 2016, at the Santa Clara Convention Center in Santa Clara, CA. Embotics is the cloud automation company for IT organizations and service providers that need to improve provisioning or enable self-service capabilities. With a relentless focus on delivering a premier user experience and unmatched customer support, Embotics is the fas...
The Internet of Things (IoT), in all its myriad manifestations, has great potential. Much of that potential comes from the evolving data management and analytic (DMA) technologies and processes that allow us to gain insight from all of the IoT data that can be generated and gathered. This potential may never be met as those data sets are tied to specific industry verticals and single markets, with no clear way to use IoT data and sensor analytics to fulfill the hype being given the IoT today.
@ThingsExpo has been named the Top 5 Most Influential M2M Brand by Onalytica in the ‘Machine to Machine: Top 100 Influencers and Brands.' Onalytica analyzed the online debate on M2M by looking at over 85,000 tweets to provide the most influential individuals and brands that drive the discussion. According to Onalytica the "analysis showed a very engaged community with a lot of interactive tweets. The M2M discussion seems to be more fragmented and driven by some of the major brands present in the...
WebRTC has had a real tough three or four years, and so have those working with it. Only a few short years ago, the development world were excited about WebRTC and proclaiming how awesome it was. You might have played with the technology a couple of years ago, only to find the extra infrastructure requirements were painful to implement and poorly documented. This probably left a bitter taste in your mouth, especially when things went wrong.
The Quantified Economy represents the total global addressable market (TAM) for IoT that, according to a recent IDC report, will grow to an unprecedented $1.3 trillion by 2019. With this the third wave of the Internet-global proliferation of connected devices, appliances and sensors is poised to take off in 2016. In his session at @ThingsExpo, David McLauchlan, CEO and co-founder of Buddy Platform, discussed how the ability to access and analyze the massive volume of streaming data from millio...