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Intel IT Labs and Big Data

Intel IT Labs and Big Data

Over the past few years, we have investigated the concept behind the big data technologies and now have much of the capabilities running in our production environment. The IT Labs continue their work to explore new and innovative solutions focused in three areas:
1. Deep Insights: This is the exploration of methodologies for combining analytics, big data technologies, and new capabilities to achieve deeper insights. Researching combinations of visualization, knowledge representation, and machine learning tools will help us uncover new ways to apply emerging analytics in our complex business environments.
2. Big Data Virtualization: Currently, the big data platforms are not easy to implement and lack effective toolsets to efficiently deploy, run, and manage this in our environment. We are looking into virtualization to bring enterprise-tested high availability and fault tolerance and more agility with automated management. Virtualization allows for separation of data and compute, letting users preserve data locality and enables mixed workload deployments. We are optimistic about this direction.
3. Big Data Search: When building systems to manage big data applications, we feel that “Search” cannot be an after-thought. Organizations want to provide a simple way to analyze petabytes of information to deliver the information that businesses require to make real-time, intelligent, and actionable decisions.

Read the full Intel IT Labs and Big Data IT Business Review.

Intel IT Labs and Big Data

Over the past few years, we have investigated the concept behind the big data technologies and now have much of the capabilities running in our production environment. The IT Labs continue their work to explore new and innovative solutions focused in three areas:
1. Deep Insights: This is the exploration of methodologies for combining analytics, big data technologies, and new capabilities to achieve deeper insights. Researching combinations of visualization, knowledge representation, and machine learning tools will help us uncover new ways to apply emerging analytics in our complex business environments.
2. Big Data Virtualization: Currently, the big data platforms are not easy to implement and lack effective toolsets to efficiently deploy, run, and manage this in our environment. We are looking into virtualization to bring enterprise-tested high availability and fault tolerance and more agility with automated management. Virtualization allows for separation of data and compute, letting users preserve data locality and enables mixed workload deployments. We are optimistic about this direction.
3. Big Data Search: When building systems to manage big data applications, we feel that “Search” cannot be an after-thought. Organizations want to provide a simple way to analyze petabytes of information to deliver the information that businesses require to make real-time, intelligent, and actionable decisions.

Read the full Intel IT Labs and Big Data IT Business Review.

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