This is the section where we get a chance to rant about latest happenings in the tech world. OneDrive can store all types of files including photos, video and documents, with accessibility to Windows PCs or mobile devices. All mobile platforms support automatic photo uploads to the OneDrive, and works closely with Microsoft Office apps.
With an Office 365 subscription, one can collaborate with people easily and even view and edit changes after they are made. OneDrive’s automatic file organization is not always accurate which is one of the flaws of OneDrive.
Dropbox is one of the favorites in cloud storage world because it’s known to be reliable and easy to use. The apps live in the file system for easy transfer of files from the computer to the cloud and even for downloading them. Google Drive combines multiple office tools with cloud storage and one gets an online word processor, spreadsheet app along with a presentation builder too. If you already have a Google account, Google Drive can be accessed easily  and one can start uploading photos, videos, documents, and other files directly on the drive.
The built-in office suite is a major advantage for editing documents, spreadsheets and effective presentations. Google Photos can help you organize photos into albums and one can actually drag and drop files into the Drive with a preview. One does have to export files to edit them in another program and even have to share storage space with Gmail. Box is for anyone who can sign up for a free account but it is the huge number of sharing and privacy options that aid business and IT users. Beyond the cloud storage setup, with support for all files, one can assign tasks, comment on other files, and even receive notifications on changes to files. Other apps from Salesforce and NetSuite can be integrated too with plug-ins for Microsoft Office and Adobe Lightroom too. OpenDrive put the user in control of the personal cloud so that you can control the content and share memories, movies, and music all the time. Store your documents, music and pictures online with instant access, sharing and collaboration, all in a simple to use, secure environment. Share files and folders with other OpenDrive users account without restrictions on file sizes. As a personal account user, one would have to shell out about 13$ per month for unlimited storage and business account users have to shell out less than 30$ per month.
Rajiv is one of the founders of Nimblechapps, a web and mobile development company based out of India. Wikibon is a professional community solving technology and business problems through an open source sharing of free advisory knowledge. The hype surrounding Big Data, which showed no signs of abating in 2012, now has big dollars backing it up. The total Big Data market reached $11.59 billion in 2012, ahead of Wikibon’s 2011 forecast.
Increased investment in Big Data infrastructure by massive Web properties – most notable Google, Facebook, and Amazon – and government agencies for intelligence and counter-terrorism purposes. In the enterprise space in particular, the combination of a better understanding of the use cases for Big Data and more mature product and service offerings resulted in a significant percentage of Big Data early adopters graduating from small, proof-of-concept projects to large-scale, production-level deployments. The Big Data market is still within the confines of the early adopter phase and is poised for significant growth.
The well-publicized lack of analytic specialists and Data Scientists armed with both the technical skill and business acumen to derive insights from large, multi-structured data sets merged from disparate sources.
A lack of understanding among enterprises on how to organize Big Data staff to best identify business requirements for Big Data projects and effectively communicate insights gleaned from Big Data to the business.
Organizational resistance to adopting Big Data analytics-driven decision-making to replace “gut instinct”-style decision-making. Vendor marketing overly focused on “speeds-and-feeds,” product features and “Big Data-washing” rather than laying out a vision for Big Data in the enterprise, articulating a path to achieve this vision, and maximizing the potential for Big Data to disrupt well-established vertical markets. Development of Big Data platforms and tools by vendors that eschew open frameworks in favor of closed, locked-down solutions.
A lack of best practices and related technologies for managing Big Data as a corporate asset, including data quality, data governance, and security platforms and tools. A dearth of Big Data application development tools and services that allow existing developers to build and customize Big Data applications using common and popular application development languages and processes. Regarding methodology, the Big Data market size, forecast, and related market-share data was determined based on extensive research of public revenue figures, media reports, interviews with vendors, venture capitalists and resellers regarding customer pipelines, product roadmaps, and feedback from the Wikibon community of IT practitioners.
Many vendors were not able or willing to provide exact figures regarding their Big Data revenue, and because many of the vendors are privately held, Wikibon had to triangulate many types of information to determine our final figures. Information types used to estimate revenue of private Big Data vendors included supply-side data collection, number of employees, number of customers, size of average customer engagement, amount of venture capital raised, and age of vendor.
It is critically important to understand how Wikibon defines Big Data as it relates to the market size overall and to revenue estimates for specific vendors in particular.
First, from a technology perspective, Wikibon defines Big Data as those data sets whose size, type, and speed-of-creation make them impractical to process and analyze with traditional database technologies and related tools in a cost- or time-effective way. Second, Wikibon believes Big Data requires practitioners to embrace an exploratory and experimental mindset regarding data and analytics, one that replaces gut instinct with data-driven decision-making, and exchanges stubbornness for a willingness to question long-held assumptions. Market-leader IBM offers by far the largest product and services portfolio by both breadth and depth.
Amazon continued and Google kicked off increasingly aggressive moves into the Big Data market.

While M&A activity was relatively tepid, two important acquisitions took place in 2012 that have the potential to impact the long-term Big Data market. Microsoft officially entered the Hadoop market in 2012 with the release of an on-premise Hadoop product - HDInsight Server for Windows – and a cloud-based Hadoop service - Windows Azure HDInsight Service. A movement to bring SQL and NoSQL together in a unified platform was firmly established in 2012.
Facebook, Google, and Amazon as well several three-letter government agencies continued to invest heavily in commodity hardware to build out massive internal Big Data infrastructures. As mentioned in the introduction of this report, Hadoop-related software and services matured rapidly in 2012, leading to increased adoption of enterprise-level products by companies in industries beyond the Web.
As a result, leading Hadoop distribution vendors Cloudera and MapR enjoyed significant revenue growth last year. Likewise, in the related NoSQL space a handful of vendors that offer commercial versions of popular open source databases enjoyed significant revenue growth as pilot projects blossomed into production deployments supporting real-time, Web-scale applications and services. Below is a cut out of Big Data revenue associated with those vendors specializing in Hadoop and NoSQL software and services. Wikibon further expects the balance of revenue generation and value to shift from Big Data infrastructure and middleware to value-add services and software over the next five years. Wikibon believes Big Data infrastructure, middleware, and technical services will become increasingly commoditized as they mature and common standards are adopted. Wikibon will be looking in more detail at the components that make up the Big Data market, shown in Figure 4. Action Item: While Big Data vendor revenue is forecast to grow significantly over the next five years, Wikibon believes that Big Data practitioners will create much more value than technology and service providers in the long-term.
The chart in the section "Big Data Revenue by Market Segment" should label the units as Millions instead of Billions. Based on what I know about the data science services industry, your estimates of % of big data revenue for Opera Solutions, Fractal Analytics and Mu Sigma are off significantly. I came across this study thanks to a 5 pages article in Le Monde that cited Wikibon as one of their source for market analysis. I notice in the revenue table that the aggregated vendor total revenue (1,223,425) is off from what it is shown on the page (1,244,602).
Jeff, came across this again this week as it was posted on LinkedIn and it got me thinking. If you must take out student loans, it’s important to understand your options so that you can borrow in the most appropriate and responsible manner possible.  Doing your homework on student loans can save you thousands of dollars in interest and fees over the life of the loan and possibly save you a lot of grief later on. Below are some of the basic pros and cons of private student loans that you should be familiar with.
Many students exhaust government loan alternatives and need to borrow more.  Private student loans may be a great option for these people.
You don’t have to fill out the Federal Application for Federal Student Aid (FAFSA), since it is only used to determine government aid. Most private student loans charge a variable interest rate rather than a fixed rate.  When rates go up, this could increase payments. Many, but not all, private student loans require a co-signer, which could be a parent, grandparent, spouse, or other person.  This person could be on the hook for the entire remaining loan balance if the student were to die or was otherwise unable to make his or her loan payments. There is generally less flexibility in the repayment of private student loans than there is with federal student loans. Rates on private student loans vary widely by lender, so you can save yourself thousands of dollars by comparing multiple loan options. Many private student loan lenders won’t give full details of the terms of the loan until after an application has been submitted.  The teaser rate and terms may say one thing, but you won’t know the actual rate and terms you qualify for until you submit an application. SimpleTuition is a site where you can easily research and compare private student loans to find the best loan possible for you.
Consider naming the person who would be responsible to pay off your loans in the event of your death (i.e. This was very helpful as my husband and I have both incurred a lot of student loans over the past few years. Files live in the cloud with the Dropbox’s website, and there are subsequent applications related to every platform. Business plans include 100 GB for 3-10 users at $6 a month and unlimited storage with minimum 3 users at $17 per month. At Nimblechapps he overlooks delivery, product development and often lends a hand to the in house development team. Factory revenue generated by the sale of Big Data-related hardware, software and services took a major step forward in 2012, growing by 59% over 2011(a). This evolution naturally required increased investment in Big Data hardware, software, and services.
For the Big Data market to reach its full potential, enterprises and vendors must overcome several obstacles. This will limit interoperability with competing and complimentary products and reduce customer choice. This list includes both Big Data pure-plays – those vendors that derive close to if not all their revenue from the sale of Big Data products and services – and vendors for whom Big Data sales is just one of multiple revenue streams.
We also held extensive discussions with former employees of Big Data companies to further calibrate our models. Projects whose processes are informed by this mindset meet Wikibon’s definition of Big Data, even in cases where some of the tools and technology involved may not.
The company also supports its Big Data practice with a well-crafted, high-level marketing campaign focused around its Smarter Planet initiative that often includes illustrations of real-world Big Data deployments. It did so mostly thanks to revenue derived from Big Data-related services, followed by sales of hardware to support Big Data deployments.

Each introduced new products and services to allow enterprises to leverage Big Data analytics and storage-as-a-service with the usual benefits associated with public Cloud services (elasticity, pay-by-the-drink, trading upfront CAPEX for monthly OPEX, etc.) Specifically, Amazon introduced RedShift, an analytic-database-as-a-service, to its portfolio and struck a deal with MapR to allow customers to run its Hadoop distribution on Amazon Web Service, among other announcements. WANdisco specializes in data replication across the WAN, which it applies to Hadoop (both its own distribution as well as Cloudera’s and Hortonworks’ distributions) with the aim of making the open source Big Data framework reliable enough to support mission critical applications. Hadapt and Teradata Aster, which kicked off this movement in 2011 continued to lead the charge but were joined by competitors Cloudera, Microsoft and others in 2012.
Facebook alone spent close to $800 million on infrastructure in just three quarters in 2012. In many cases, companies that had previously deployed community (read: free) versions of vendor Big Data software bundles for proof-of-concept projects began upgrading to paid software and services to support production-level deployments.
The company’s NoSQL document store is in use at Bank Of America, the Defense Intelligence Agency and Warner Brothers, among other household names in the media and financial services industries. Note that these vendors account for total Big Data revenue of $272 million and are growing at a faster percentage rate than the rest of the Big Data market. Looking beyond 2013, Wikibon forecasts the total Big Data market to approach $50 billion by 2017, which translates to a 31% compound annual growth rate over the five-year period 2012-2017. As noted, hardware revenue accounts for 37% of Big Data revenue and a large portion of software and services revenue is associated with infrastructure software and technical services that tie Big Data platforms and data together.
Practitioners will increasingly look to NoSQL and in-memory database software, streaming analytic platforms, vertically focused analytical and transactional applications and application development platforms (both on-premise and Cloud-based) and associated consulting and professional services to address specific, high-value business problems and opportunities. When selecting vendors to support Big Data initiatives, therefore, CIOs and Big Data practitioners must evaluate the products and services on offer in the context of how best to monetize Big Data to achieve competitive advantage.
Upon further review and extensive feedback from the Wikibon community, it was decided that the original figure underestimated the level of revenue generated by original device manufacturers.
We are close to $8 Million with all revenue coming from open source and increasing part from big data. The report from Wikibon is based on primary research with specific data by vendor that adds up to the market total in a "transparent" way.
I do believe the India pureplays have much to gain and will be investing in differentiating themselves with Big Data Analytics & Cloud. It is yet another reason, and even more proof, that WikiBon is such a virtuous organization. You simply enter the amount you want to borrow and some basic information about yourself and then the site displays a customized list of options for you. Here is a list of the most popular cloud storage tools, with their strengths and weaknesses.
Feedback from the Wikibon community included multiple reports of $100 million+ deals from both government and commercial buyers. While a detailed discussion of these obstacles is outside the purview of this report, they are worth noting.
The biggest criticism of IBM from practitioners is that the company’s portfolio is so wide and deep it causes confusion. HP by its sheer size is in a position to impact and participate in a number of Big Data deployments. Consolidated across vendors, professional and cloud services revenue accounted for $5 billion of total 2012 Big Data revenue. VMware had already begun efforts to apply virtualization technology to Hadoop, and the acquisition of CETAS gives the vendor a more comprehensive Big Data portfolio. Microsoft also announced PolyBase, which aims to allow the SQL Server Parallel Data Warehouse to execute SQL queries against data stored in Hadoop. This spending is reflected in Big Data revenue for the original device manufacturer (ODM) category that appears at the bottom of the table. While the global economic outlook is for slow to stagnant growth over this period, Wikibon believes the Big Data market will not be severely impacted and may, in fact, benefit from enterprises needing “to do more with less,” which effective Big Data analytics facilitates.
This includes evaluating “speeds and feeds” and other product features but should also include evaluating how well vendors can assist enterprises in adopting a sustainable culture of data-driven decision-making. I don't see any reference to market share data in the Transparent Research report description so it's not clear where there baseline comes from. They have doubled their revenue & improved market share in last 6 years and are giving stiff competition to global MNCs. This is exactly the kind of info I need for context and I know many in large enterprises need this too. In which category of your market forecast would you place these data collection services - perhaps SaaS, app software, professional services, or a mix? IBM combats this confusion by initiating many Big Data customer engagements through its professional services division.
Google finally got into the Big Data game by productizing Big Data tools and technologies, such as BigQuery, it has long used internally, and likewise introduced MapR as a service via Google Compute Engine. The creation of the Pivotal Initiative further indicates that VMware and EMC are continuing to invest in Big Data for the long-term. Specifically, Facebook and others like it purchase, configure, and deploy off-the-shelf hardware from ODM’s such as Quanta, rather then purchasing commodity machines from vendors such as Dell or HP, to support the majority of their operations. Probably because we are mainly a European vendor, but we start to have some customer in the US too.
A challenge and area of focus for IBM moving forward is to continue to articulate its Big Data vision in a way that focuses on industry solutions and not point products.
Services that make data sets available to enterprises and other service providers are a key enabler to the Data Economy.

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