Leveraging the Power of the Cloud to Protect VMware Data

While virtualization has provided storage pros with the ability to overcome the limitations of on-premises physical servers—gaining more flexibility and optimization for compute and storage utilization—the data protection strategy continues to be complex and expensive. With over 90% of organizations moving to the cloud, IT professionals are looking for a unified, scalable and flexible solution to protect their virtualized workloads on-premises and in the cloud, while freeing their applications from the limits of legacy infrastructure.
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Spotlight

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A Live Look at the Cloud Computing Bootcamp

Cloud computing is the key to the global platforms and apps we all rely on. In this preview, you’ll learn how to turn your passion for the cloud into a career.
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Data Virtualization to Survive a Multi and Hybrid Cloud World

Hybrid cloud computing is slowing becoming the standard for businesses. The transition to hybrid can be challenging depending on the environment and the needs of the business. A successful move will involve using the right technology and seeking the right help. At the same time, multi-cloud strategies are on the rise. More enterprise organizations than ever before are analyzing their current technology portfolio and defining a cloud strategy that encompasses multiple cloud platforms to suit specific app workloads, and move those workloads as they see fit.
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Data for the Clinical Journey: Pairing Analytics with Health Cloud for Data-Driven Success

All the stakeholders within the Health and Life Sciences Industry find the need for a complete view of the trends affecting their patients in addition to personal data and history. Healthcare providers, insurers, and agencies even while having different roles in delivering care tend to face many of the same challenges.
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Expert Panel: Automating Data and Analytics in the Cloud

As data and analytics environments become increasingly complex, organizations can no longer afford to perform many operations manually. According to TDWI research, automation (in general) is one of the top three priorities for analytics. We see automation occurring throughout the data and analytics life cycle. Automation increasingly leverages embedded AI/ML algorithms (i.e., infused in the software) to help perform tasks such as profiling and cleansing data, identifying sensitive data, data mapping, surfacing insights, or building machine learning models.
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