Welcome!

@CloudExpo Authors: Liz McMillan, Zakia Bouachraoui, Yeshim Deniz, Pat Romanski, Elizabeth White

Blog Feed Post

65% of all packaged advanced analytics applications will have Hadoop embedded by 2015

By

big-data-620x400Gartner projects that by 2015, 65 percent of packaged analytic applications with advanced analytics will come with Hadoop embedded.  Sounds rational/logical/realistic to me.  Here is more from their 24 January 2013 release on the topic:

“New business insights and improved decision making with greater finesse are the key benefits achievable from turning more data into actionable insights, whether that data is from an increasing array of data sources from within or outside of the organization,” said Daniel Yuen, research director at Gartner. “Different technology vendors, especially niche vendors, are rushing into the market, providing organizations with the ability to tap into this wider information base in order to make sounder strategic and prompter operational decisions.”

By 2015, 65 percent of packaged analytic applications with advanced analytics will come embedded with Hadoop.

Organizations realize the strength that Hadoop-powered analysis brings to big data programs, particularly for analyzing poorly structured data, text, behavior analysis and time-based queries. While IT organizations conduct trials over the next few years, especially with Hadoop-enabled database management system (DBMS) products and appliances, application providers will go one step further and embed purpose-built, Hadoop-based analysis functions within packaged applications. The trend is most noticeable so far with cloud-based packaged application offerings, and this will continue.

“Organizations with the people and processes to benefit from new insights will gain a competitive advantage as having the technology packaged reduces operational costs and IT skills requirements, and speeds up the time to value,” said Bill Gassman, research director at Gartner. “Technology providers will benefit by offering a more competitive product that delivers task-specific analytics directly to the intended role, and avoids a competitive situation with internally developed resources.”

Gartner provides more detailed analysis in their report titled ”Predicts 2013: Business Intelligence and Analytics Need to Scale Up to Support Explosive Growth in Data Sources.” The report is available on Gartner’s website at http://www.gartner.com/resId=2269516.

To track these trends closely be sure you have signed up for our CTOvision.com Daily.  You can also choose to select weekly editions of our reporting including the Government Big Data Weekly and the Analytical Tools Report.

Read the original blog entry...

More Stories By Bob Gourley

Bob Gourley writes on enterprise IT. He is a founder of Crucial Point and publisher of CTOvision.com

CloudEXPO Stories
With more than 30 Kubernetes solutions in the marketplace, it's tempting to think Kubernetes and the vendor ecosystem has solved the problem of operationalizing containers at scale or of automatically managing the elasticity of the underlying infrastructure that these solutions need to be truly scalable. Far from it. There are at least six major pain points that companies experience when they try to deploy and run Kubernetes in their complex environments. In this presentation, the speaker will detail these pain points and explain how cloud can address them.
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-centric compute for the most data-intensive applications. Hyperconverged systems already in place can be revitalized with vendor-agnostic, PCIe-deployed, disaggregated approach to composable, maximizing the value of previous investments.
When building large, cloud-based applications that operate at a high scale, it's important to maintain a high availability and resilience to failures. In order to do that, you must be tolerant of failures, even in light of failures in other areas of your application. "Fly two mistakes high" is an old adage in the radio control airplane hobby. It means, fly high enough so that if you make a mistake, you can continue flying with room to still make mistakes. In his session at 18th Cloud Expo, Lee Atchison, Principal Cloud Architect and Advocate at New Relic, discussed how this same philosophy can be applied to highly scaled applications, and can dramatically increase your resilience to failure.
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by sharing information within the building and with outside city infrastructure via real time shared cloud capabilities.
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.