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Big Data, Big Analytics, and Big Insights

Big Opportunity

Every day 2.5 quintillion (1018) bytes of data are created, and 90% of the data in the world today has been generated in the last couple of years alone. Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data, which takes too much time and cost too much money to load into a traditional data store for analysis. The impact of big data is significantly cross-cutting, for both the business and technology management at the provider and consumer sides.


2012 tends to be a big year for big data and big analytics. To effectively explore the massive amounts of data being produced in our world will help unveil answers to some of society’s most vexing problems. Unconventional technologies and platforms have emerged in recent years, such as Hadoop and NoSQL. The advanced analytics tap into the power of these maturing tools to process unstructured, semi-structured, and structure data, unlocking new insights that were never before possible. However, there are still a number of challenges, issues, constraints, barriers, and pitfalls in this evolving space. Difficulties include big data capture, transport, transform, ingest, store, search, share, analyze, aggregate, mine, classify, summarize, render, and visualize.

More Stories By Tony Shan

Tony Shan works as a senior consultant, advisor at a global applications and infrastructure solutions firm helping clients realize the greatest value from their IT. Shan is a renowned thought leader and technology visionary with a number of years of field experience and guru-level expertise on cloud computing, Big Data, Hadoop, NoSQL, social, mobile, SOA, BI, technology strategy, IT roadmapping, systems design, architecture engineering, portfolio rationalization, product development, asset management, strategic planning, process standardization, and Web 2.0. He has directed the lifecycle R&D and buildout of large-scale award-winning distributed systems on diverse platforms in Fortune 100 companies and public sector like IBM, Bank of America, Wells Fargo, Cisco, Honeywell, Abbott, etc.

Shan is an inventive expert with a proven track record of influential innovations such as Cloud Engineering. He has authored dozens of top-notch technical papers on next-generation technologies and over ten books that won multiple awards. He is a frequent keynote speaker and Chair/Panel/Advisor/Judge/Organizing Committee in prominent conferences/workshops, an editor/editorial advisory board member of IT research journals/books, and a founder of several user groups, forums, and centers of excellence (CoE).

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.