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Apache Hadoop has actually become the dominant platform for Big Data analytics in the last few years, thanks to its flexibility, integrity, scalability, and ability to match the requirements of developers, web startups, and business IT. A rapid and economic means to leverage the huge quantities of data created by new sources such as social networking sites, mobile sensing units, social media, and Internet of Ordeals devices, Hadoop has actually ended up being the favored system for storage space and analytics of huge unstructured datasets.

Originally established in 2003 by data scientists at Yahoo!, Hadoop was quickly welcomed by the open source community, in addition to consumer-facing Internet giants such as Google and Facebook. Recently, Hadoop has actually been embraced by ventures that similarly need to obtain actionable insight from Big Data created by brand-new data sources, technology innovations, cloud support services, and business opportunities. IDC has actually predicted the Hadoop software market will be worth $813 million by 2016.

Hadoop is a game changer for enterprises, transforming the economics of massive information analytics. It eliminates information silos, and lessens the need to move information in between storage space and analytics software program, giving businesses with a much more all natural sight of their clients and operations, causing quicker and a lot more effective company ideas. Its extensibility and countless combinations can power a new generation of data-aware business applications.

The software application’s “refreshingly distinct approach to information administration is transforming exactly how firms save, process, evaluate and share large data,” according to Forrester analyst Mike Gualtieri. “Forrester believes that Hadoop will end up being essential facilities for huge business.”.

For companies utilizing proprietary information solutions and personnel familiar with SQL analytics tools, transitioning to Hadoop could be difficult, in spite of its several advantages. Combination with existing infrastructure could provide a significant difficulty. To this end, Critical supplies its enterprise-grade Hadoop distribution Pivotal HD as either a standalone product or part of the Pivotal Big Data Collection.

Crucial HD builds on Hadoop’s solid foundation by including features that boost business adoption and usage of the platform. It allows business Data Lake, permitting companies to introduce their existing analytics tools to their data. Essential HD is the Foundation for business Data Lake supplying the World’s Many Advanced Real-Time Analytics System with GemFire XD, and the most extensive set of Advanced Analytical Toolsets with HAWQ, MADlib, OpenMPI, GraphLab or even Spring XD. Showcasing HAWQ, the world’s fastest SQL query engine on Hadoop, Pivotal HD speeds up information analytics houses, leverages alreadying existing skillsets, and significantly broadens Hadoop’s capacities. Crucial GemFire brings live analytics to Hadoop, allowing companies to procedure and make essential company choices instantly.

While leveraging Hadoop’s tested perks, Essential HD includes features that ease adoption, boost efficiency, and offer robust administration tools. It sustains leading information science devices such as MADlib, GraphLab (OpenMPI), and User-Defined Functions, including support for prominent languages such as R, Java, and Python. Essential HD additionally integrates with Spring season environment buildings such as Springtime XD, alleviating the advancement of data-driven applications and solutions.

Allowing companies to collect and take advantage of both organized and disorganized data kinds, Pivotal HD makes it possible for a flexible, fault-tolerant, and scalable Business Information Lake. Pivotal’s engineers, several of which were indispensable to Hadoop’s development and development, have actually developed an enterprise-grade Hadoop circulation. Find out more regarding their proceeded work on Pivotal HD on the Critical blog site.



Books and other resources to learn R

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From Amy’s Page

12 Books and other resources to learn R

This article was originally posted on UCAnalytics. Link to full version is provided at the bottom.

1. R for Reference

r for everyoneR for Everyone: Advanced Analytics and Graphics – Jared P. Lander

YOU CANalytics Book Rating 5 Stars (5 / 5)

Jared Lander, in his book, wastes no time on basic graphic (comes pre-installed with R), but jumps directly to ggplot2 package (a much advanced and sleek graphical package). This sets the tone for this book i.e. don’t learn things you won’t use in real life applications later. I will highly recommend this book for a fast paced experience to learn R.

R in Action

R in Action - Robert Kabacoff

YOU CANalytics Book Rating 5 Stars (5 / 5)

Here is another exceptional book to start learning R on your own. I must say Robest Kabacoff, the author of this book, has done a phenomenal job with this book. The organization of the book is immaculate and the presentation is friendly. I will highly recommend either this book or R for Everyone to start your journey to learn R.

The r bookThe R Book Michael J. Crawley

YOU CANalytics Book Rating 4.8 Stars (4.8 / 5)

With close to a thousand pages and vast coverage, ‘The R Book’ could be called the Bible for R.  This book starts with simple concepts in R and gradually move to highly advanced topics. The breadth of the book can be estimated through the presence of dedicated chapters on topics as diverse as data-frames, graphics, Bayesian statistics, and survival analysis. Essentially this is a must have reference book for any wannabe R programmer. But for a beginner the thickness of the book could be intimidating.

2. R with Theory

R StatsAn Introduction to Statistical Learning: with Applications in R - Gareth James et al.

YOU CANalytics Book Rating 5 Stars (5 / 5)

This book is a high quality statistical text with R as the software of choice. If you want to be comfortable with fundamental concepts in parallel with learning R, then this is the book for you. Having said this, you will love this book even if you have studied advanced statistics. The book also covers some advanced machine learning concepts such as support machine learning (SVM) and regularization. A great book by all means.

machine learning with RMachine Learning with R Brett Lantz

YOU CANalytics Book Rating 4.5 Stars (4.5 / 5)

If you want to learn R from the machine learning perspective, then this is the book for you. Some people take a lot of interest in fine demarcation between statistics and machine learning; however for me there is too much overlap between the topics. I have given up on the distinction as it makes no difference from the applications perspective. The book introduces R-Weka package – Weka is another open source software used extensively in academic research.

3. R with Applications

 r and data miningR and Data Mining: Examples and Case Studies – Yanchang Zhao

YOU CANalytics Book Rating 4.3 Stars (4.3 / 5)

There are other books that use case studies approach for readers to learn R. I like this book because of the interesting topics this book covers including text mining, social network analysis and time series modeling. Having said this, the author could have put in some effort on formatting of this book which is pure ugly. At times you will feel you are reading a masters level project report while skimming through the book. However, once you get over this aspect the content is really good to learn R.

R rattleData Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery (Use R!) - Graham Williams

YOU CANalytics Book Rating 4.2 Stars (4.2 / 5)

Rattle is no SAS E-miner or SPSS modeler (both commercial GUI based data mining tools). However trust me, apart from a few minor issues Rattle is not at all bad. The book is a great reference to Rattle (a GUI add on package for R to mine data) for data mining. I really hope they keep working on Rattle to make it better as it has a lot of potential.

 4. R Graphics and Programming

GGplot2ggplot2: Elegant Graphics for Data Analysis (Use R!) – Hadley Wickham

YOU CANalytics Book Rating 4 Stars (4 / 5)

‘ggplot 2′ is an exceptional package to create wonderful graphics on R. It is much better than the base graphics that comes pre-installed with R, so I would recommend you start directly with ggplot 2 without wasting your time on base graphics. ‘R for everyone’, the first book we discussed, has a good introduction to ggplot. However, if you want to get to further depths of ggplot-2 then this is the book for you.

Though I prefer ggplot 2, Lattice is another package at par with ggplot 2. A good book to start with Lattice is ‘Lattice: Multivariate Data Visualization with R (Use R!) by Deepayan Sarkar’.

Read full list.

Additional links

Emerging Storage

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Emerging Storage, VMware And Pivotal Drive EMC’s Q2 Earnings

Trefis TeamTrefis Team , Contributor

EMC announced its second quarter earnings on July 23, reporting a 5% year-on-year growth in net revenues to $5.9 billion. The company’s services revenues rose by almost 9% over the prior year quarter to $2.6 billion while its product revenues stayed flat at about $3.3 billion. Much of the growth was driven by VMware (+17%), Pivotal (+28%) and RSA Security (7%) while core information storage revenues remained nearly flat at $4 billion.

EMC’s market share in external storage systemsdeclined from 30.2% in Q1 2013 to 29.1% in the first quarter of 2014, according to a recent report by IDC. This was the first quarter since 2008 in which EMC’s market share declined year-over-year. EMC’s revenues from external storage systems in Q1 declined by almost 9% while the industry-wide decline was about 5%. However, EMC’s revenues in Q2 grew higher than the industry average, due to which the company gained share in the market.

Weakness in its core business led to market speculation prior to earnings about EMC spinning off VMware and Pivotal. The Wall Street Journal reported that external pressure from EMC’s large institutional investors could lead the company to spin off some of the fastest-growing businesses within the company such as VMware and Pivotal. However, EMC’s management refuted the speculation and stood by its “federation” business model, wherein some of the acquired companies operate as separate entities while they still collaborate on products for large clients. The company believes that its current setup is ideal for growth for both EMC and the acquired companies.

We have a $30 price estimate for EMC, which is roughly in line with the current market price.

See our full analysis for EMC’s stock

Key Areas Of Growth:

Emerging Storage

EMC’s Emerging Storage products such as XtremIO, Isilon, Atmos and VPLEX were largely responsible for the growth in hardware sales during the past few quarters. The Emerging Storage sub-segment grew by 51% year-over-year (y-o-y) in Q1 2014, which the company attributed to a strong customer response for these products. Despite strong y-o-y growth, the revenues generated by emerging storage solutions stayed flat over Q1. The company attributed this to intermittent demand for some large individual orders. The company expects strong growth for emerging storage solutions on the back of solid demand for software-defined storage, Big Data analytics, cloud storage and flash arrays in the coming quarters.


VMware’s revenues grew by 17% y-o-y to $1.45 billion for the June quarter with growth coming from both product licenses revenues (+16%) and services revenues (+18%). However, VMware’s gross margin within EMC declined by 180 basis points over the prior year quarter to 87.8%. The decline in VMware’s margins led EMC’s overall gross margin to decline by 40 basis points to 62.1%. EMC has invested over $6 billion in acquisitions and internal developments since 2012, of which a significant portion was attributable to VMware related products. These acquisitions included software-defined networking leader Nicira and mobility management leader AirWatch. All the acquisitions will show up as losses on the income statement this year. However, management believes that margins are likely to improve in the future quarters (read: SDN, Hybrid Clouds And AirWatch Help VMware Post Strong Q2 Results).


Pivotal is among the fastest-growing divisions within the company, with 40% y-o-y growth in the first quarter. Although the growth rate was lower than the previous quarter at 29%, the number of orders rose by over 50%. Additionally, Pivotal’s margins expanded from the March quarter. Pivotal’s platform consists of new generation data fabrics, application fabrics and a cloud-independent Platform-as-a-Service to support cloud computing and Big Data applications, which have started gaining traction among customers. Management mentioned that some of Pivotal’s growth may not be immediately realized in the numbers since it is building out a subscription-based revenue stream, which is likely to be beneficial in the long run.

RSA Security

RSA Security, EMC’s information security division, grew by over 11% to almost $1 billion in 2013. The growth continued in the first half of 2014, but the rate of growth was lower than 2013 at about 6% y-o-y. The information security industry is growing, with customers allocating more of their security budgets to intelligence-driven analytics, where RSA Information Security excels, rather than static prevention.


Microsoft Tries Appliances to Build Clouds

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Microsoft Tries Appliances to Build Clouds


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The Surface tablet, Xbox One gaming console and the plethora of peripherals are not the only pieces of hardware in the Microsoft stable. The company is making plans to launch new storage and hybrid Azure cloud devices to expand their capabilities and market share.

According to ZDNet, Microsoft is ramping up a storage appliance, aptly named Azure StorSimple 8000, which connects to the Azure cloud and is based on its 2012 acquisition StorSimple. The appliance allows users to store data that’s most used in the local storage while assigning and indexing lesser used files in the cloud.


Microsoft Tries Appliances to Build Clouds

The Azure StorSimple appliance, slated for release in August, will connect Azure StorSimple Manager, which will provide users with simplified access and management to locally and remotely stored files.

Microsoft will continue to sell and support the StorSimple 5000 and 7000 series appliances, which also connect to the Azure cloud but do not integrate with Azure StorSimple Manager.

Unlike other appliances in the software giant’s fold, Microsoft is looking to channel partners – specifically systems integrators – to sell and deploy the StorSimple devices in enterprise and midmarket accounts for disaster recovery, primary and secondary storage, and platforms for application management.

Separate from StorSimple, Microsoft is reportedly gearing up for another run at the Azure in a box strategy. Plans for an Azure private cloud appliance, reportedly being developed under the code name “San Diego,” will provide enterprises with on-premises cloud, network and storage resources. Essentially, Microsoft is attempting to provide enterprises with the same cloud-based Azure functionality in their own data center.

Since 2010, Microsoft has attempted to release Azure appliances. The initial cuts were announced with OEM partners such as Hewlett-Packard, Dell and Fujitsu. Only Fujitsu ended up releasing a commercial product. ZDNet reports the original program puttered out in late 2012 even though no official announcement was made.

The new Azure appliance versions will reportedly come from and be supported by Microsoft, and sold through its systems integrator channel.

While pushing deeper into hardware to support its cloud strategy, Microsoft insists plenty of room exists in the market for its appliances and services as well as similar offerings by its traditional OEM partners. Nevertheless, the expanding hardware portfolio does provide further evidence that Microsoft is increasingly a competitor to companies such as Hewlett-Packard, Dell, Lenovo, EMC and IBM.

And, unlike its Surface tablets, Microsoft seems to have no issue in selling and support hardware devices through its B2B channels.

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IBM Provides Cloud Services to California State Agencies
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Dropbox for Business Beefs Up Security


IBM Provides Cloud Services to California State Agencies

24/7/365 Network Uptime!

By Barry Levine. Updated July 24, 2014 1:57PM 









There’s a big, new cloud Relevant Products/Services coming to California, powered by IBM. The tech giant said Thursday it will be supplying cloud services for more than 400 state and local agencies.The service, called CalCloud, is the first of its kind in the U.S. at a state level. It will allow data Relevant Products/Services and programs to be stored and made available to all participating agencies, which will only pay for thecomputing Relevant Products/Services workload they actually use.

The cloud services need to comply with a range of requirements from such federal agencies as the IRS and the Social Security Administration, not to mention HIPAA (the Healthcare Insurance Portability and Accountability Act) and the security Relevant Products/Services standards of the National Institute of Standards.

‘Important Step’

Through CalCloud, agencies can now share a common pool of computing resources that the California Department of Technology said would be more efficient than the current setup. Nearly two dozen departments have requested IT Relevant Products/Services services via CalCloud.

Marybel Batjer, secretary of the Government Operations Agency, said in a statement that CalCloud “is an important step towards providing faster and more cost-effective IT services to California state departments and ultimately to the citizens of California.”

IBM will be supplying and managing the infrastructure Relevant Products/Services of CalCloud, and the state’s Department of Technology will take care of the other aspects. Big Blue also said it will work with the state to transfer knowledge and best practices relating to security and systems integration with the department.

As with other cloud services, this pay-for-use arrangement will enable the state agencies to scale Relevant Products/Services up or down the resources they need for variable workloads. It also provides immediate and round-the-clock access to such configurable resources as compute, storage Relevant Products/Services,network Relevant Products/Services and disaster recovery services.

High Performance, Watson

IBM has been rapidly building up its cloud services, and creating more than a hundred software Relevant Products/Services-as-a-service solutions for specific industry needs. The CalCloud project will likely become the basis for similar offerings to other states, as well as to other governments worldwide.

In other IBM news, the company said Wednesday that it will be making high performance computing more accessible through the cloud to clients that need additional capabilities for big data and other computationally intensive workloads.

Very high data throughput speeds will be enabled from IBM’s SoftLayer company, using InfiniBand networking technology to connect SoftLayer bare metal servers. InfiniBand is a networking architecture that delivers up to 56 Gbps.

SoftLayer CEO Lance Crosby said in a statement that “our InfiniBand support is helping to push the technological envelope while redefining how cloud computing can be used to solve complex business Relevant Products/Servicesissues.”

Also on Wednesday, IBM and financial services firm USAA announced that IBM Watson intelligence Relevant Products/Services-as-a-service technology will now be employed for USAA members. It is the first commercial use of Watson in a consumer-facing role. Watson will be used in a pilot project to help military men and women transition from military to civilian life.




Box Raises More Money

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Box Raises More Money, Cloud Questions




Cloud storage and content management company Box is fast becoming a focal point of the cloud computing era. While other cloud ventures such as Salesforce.com and NetSuite have become productive service providers, Box plods along with high cash-burn rates and an indeterminate exit strategy.

Yesterday, Box announced it raised another $150 million in fresh venture funding, adding to its $80 million in cash reserves and bringing its total investment backing to $450 million. The company is now worth, by some estimates, $2.4 billion, even though its revenues are somewhere around $200 million.

What makes Box an interesting study is its expenses. Until recently, the company spent much more on marketing and communications than anything else in its operations.According to Forbes, Box spent $171 million on sales and marketing in 2013 – nearly a third more than its total revenue. The company says its business model, which relies on adding accounts and subscribers, requires heavy investments in sales, marketing and infrastructure.

Box’s high expense has long been a sour spot. The company is showing signs of reining in expenses and expanding sales faster than spending. In the first quarter of 2014, marketing spending was still up 40 percent over the same quarter in 2013, but the sales doubled.

The challenge Box faces is the same as for many cloud service providers. Cloud revenues compound over time, and deferred revenue counts more than point-in-time sales. Box is counting nearly $90 million in deferred revenue from the first quarter – double over the same period in 2013 – and it’s added more than 5,000 paid corporate accounts. All cloud service providers see weakness in revenues while building their base. If they’re manage the transition period, they will hit an inflection point where compounding recurring revenue will exceed and accelerate past expenses.

Another company experiencing this phenomenon is Adobe. In 2013, Adobe abandoned its traditional software licensing model to embrace cloud subscriptions. Initially, Adobe revenues and profits plummeted to the point where alarms were going off on Wall Street and among partners and users. The precipitous dip made many question whether Adobe could whether the financial transition.

Today, Adobe is profitable and growing. Its compound recurring revenue – based on nearly 2.2 million paid users – is generating positive cash flow. And the company expects to exceed 3.3 million paid subscribers before the end of 2014.

Box is a bit different than many cloud providers, as it supports millions more unpaid users than paid subscribers. This puts a burden on the company to build around that broader base with infrastructure and support, which adds expenses. However, Box may prove the broader base is worth the expense, as they contribute to the conversion of net-new paid accounts.

The ultimate lesson Box may prove is that marketing makes a difference in building cloud brands. If Box turns the corner, goes public and becomes another cloud powerhouse, it will change the rules on what it takes to build a successful cloud-era business: loud and persistent marketing and communications.

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Big Data: The 5 Vs Every person Should Know

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Big Data: The 5 Vs Every person Should Know that all are essential


Big Data is a huge point. It will transform our globe entirely and is not a passing craze that will certainly disappear. To know the sensation that allows data, it is usually explained utilizing 5 Vs: Volume, Velocity, Range, Veracity and Value

I assumed it may be worth simply restating what these 5 Vs are, in simple and easy language:.

Quantity refers to the vast amounts of data created every secondly. Just think about all the emails, twitter messages, photos, video clips, sensing unit data etc. we create and discuss every second. We are not speaking Terabytes yet Zettabytes or Brontobytes. On Facebook alone we send out 10 billion messages daily, click the “like’ button 4.5 billion times and upload 350 million new images every day. If we take all the information generated in the world between the start of time and 2008, the exact same amount of data will certainly soon be generated every min! This increasingly makes data sets too big to shop and assess utilizing standard database technology. With large information technology we could now store and use these data sets with the aid of distributed systems, where parts of the data is saved in various locations and combined by software program.

Speed describes the speed at which brand-new information is generated and the speed at which data moves around. Merely think of social networks messages going viral in seconds, the rate at which bank card transactions are looked for fraudulent activities, or the nanoseconds it takes trading systems to analyze social networks networks to pick up signals that set off choices to acquire or sell shares. Large data modern technology enables us now to evaluate the data while it is being produced, without ever putting it into data sources.


Selection refers to the various kinds of information we could now utilize. In the past we concentrated on structured information that neatly matches tables or relational databases, such as financial information (e.g. sales by item or area). Actually, 80 % of the world’s data is now disorganized, and therefore can’t easily be put into tables (consider pictures, video sequences or social media sites updates). With huge information technology we could now utilize differed sorts of information (structured and unstructured) including messages, social networks talks, photos, sensing unit data, video clip or voice recordings and bring them along with even more standard, organized information.

Accuracy describes the messiness or trustworthiness of the information. With several kinds of big data, top quality and precision are less manageable (merely think about Twitter posts with hash tags, abbreviations, typos and colloquial speech and also the reliability and precision of content) however large data and analytics innovation now enables us to work with these type of information. The quantities usually offset the absence of high quality or accuracy.

Worth: Then there is another V to think about when checking out Big Data: Worth! It is all well and great having accessibility to huge data however unless we could turn it into value it is pointless. So you could securely say that ‘worth’ is one of the most vital V of Big Data. It is very important that businesses make a business situation for any sort of try to collect and leverage large information. It is so simple to come under the talk catch and plunge into large data initiatives without a clear understanding of costs and benefits.

I have assembled this little presentation for you to make use of when talking about or discussing the 5 Vs of big data:.


Big Data: The 5 Vs Every person Should Know that all are essential

Big Data: The 5 Vs Everyone Needs to Know

Passive Income Systems

Big Data: The 5 Vs Everyone Need to Know that are essential


Big Data is a large point. It will certainly alter our globe totally and is not a passing fad that will vanish. To recognize the sensation that allows information, it is typically described utilizing 5 Vs: Quantity, Velocity, Assortment, Honesty and Worth

I assumed it may be worth simply restating just what these 5 Vs are, in plain and simple language:.

Volume describes the substantial amounts of data created every secondly. Simply think of all the e-mails, twitter messages, photos, video clips, sensor information and so on we create and share every second. We are not chatting Terabytes however Zettabytes or Brontobytes. On Facebook alone we send 10 billion messages every day, click the “like’ button 4.5 billion times and upload 350 million brand-new pictures every single day. If we take all the information produced on the planet in between the start of time and 2008, the exact same amount of data will soon be created every min! This increasingly makes data sets as well large to store and examine using typical database innovation. With big information modern technology we can now hold and utilize these data sets with the help of dispersed devices, where parts of the data is held in various areas and brought together by software program.

Big Data: The 5 Vs Everyone Needs to Know

Speed refers to the speed at which brand-new data is produced and the rate at which data moves around. Merely think of social media messages going viral in seconds, the speed at which credit card transactions are looked for deceitful tasks, or the milliseconds it takes trading systems to assess social networking sites networks to get signals that trigger decisions to get or market shares. Big information modern technology enables us now to assess the information while it is being generated, without ever putting it into databases.

Range refers to the different sorts of information we could now use. In the past we concentrated on structured information that properly matches tables or relational databases, such as economic data (e.g. sales by product or area). Actually, 80 % of the globe’s data is now disorganized, and for that reason can’t quickly be embeded tables (consider photos, video clip sequences or social networks updates). With large data modern technology we can now take advantage of differed kinds of information (structured and disorganized) consisting of messages, social networking sites chats, pictures, sensor data, video or voice recordings and bring them in addition to more conventional, structured data.

Honesty refers to the messiness or credibility of the data. With numerous forms of huge data, quality and reliability are much less controlled (merely think of Twitter posts with hash tags, abbreviations, typos and colloquial speech in addition to the reliability and accuracy of content) yet big information and analytics technology now enables us to collaborate with these sort of data. The volumes often make up for the absence of top quality or reliability.

Value: Then there is another V to take into account when taking a look at Big Information: Value! It is all well and great having accessibility to big data however unless we can turn it into worth it is ineffective. So you can securely say that ‘value’ is the most crucial V of Big Information. It is essential that businesses make a company situation for any type of attempt to collect and leverage large data. It is so easy to fall into the talk catch and start large information campaigns without a clear understanding of prices and perks.

I have put together this little discussion for you to make use of when talking about or talking about the 5 Vs of big information:

Big Data: The 5 Vs Everyone Needs to Know 



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