Defining the Data Lake
“Big data” is an idea as much as a particular methodology or technology, yet it’s an idea that is enabling powerful insights, faster and better decisions, and even business transformations across many industries. In general, big data can be characterized as an approach to extracting insights from very large quantities of structured and unstructured data from varied sources at a speed that is immediate (enough) for the particular analytics use case.
Software-defined architectures have transformed enterprises to become more application-centric. With application owners
seeking public-cloud-like simplicity and flexibility in their own data centers, IT teams are under pressure to reduce wait times to
Legacy load balancing solutions force network architects and administrators to purchase new hardware, manually configure
virtual services, and inefficiently overprovision these appliances. Simultaneously, new infrastructure choices are also enabling IT
teams to re-architect applications into autonomous microservices from monolithic or n-tier constructs. These transformations
are forcing organizations to rethink load balancing strategies and application delivery controllers (ADCs) in their infrastructure.
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As workplace researcher Robert Propst observed in
A Facility Based on Change, “Not only must we accept
change, we must adjust to accelerated growth.” If your
office doesn’t reflect who you are now and who you hope to
become in the future, accelerated growth can feel especially
painful. Whoever you are, you can’t escape change. But we
think that workplaces can actually be catalysts for growth
without all the growing pains. Whether you’re trying to spark
innovative thinking or encourage people to work together
more efficiently, you can reach your goals with a workplace
tailored to your unique needs.
We’ve been capturing workplace data and transforming it
into growth-enhancing insight since Propst began studying
the connections between people, work, and the workplace
nearly 50 years ago. This legacy of workplace research—in
combination with the research we do today—informs Living
Office®, a research-based placemaking approach that
ignites powerful workplace transformations. With Living
Software-defined architectures have transformed enterprises to become more application-centric. With application owners seeking public-cloud-like simplicity and flexibility in their own data centers, IT teams are under pressure to reduce wait times to provision applications.
Legacy load balancing solutions force network architects and administrators to purchase new hardware, manually configure virtual services, and inefficiently overprovision these appliances. Simultaneously, new infrastructure choices are also enabling applications to be re-architected into autonomous microservices
from monolithic or n-tier constructs. These transformations are forcing organizations to rethink load balancing strategies and application delivery controllers (ADCs) in their infrastructure.
The most recent decade has seen rapid advances in connectivity, mobility, analytics, scalability, and data, spawning what has been called the fourth industrial revolution, or Industry 4.0. This fourth industrial revolution has digitalized operations and resulted in transformations in manufacturing efficiency, supply chain performance, product innovation, and in some cases enabled entirely new business models.
This transformation should be top of mind for quality leaders, as quality improvement and monitoring are among the top use cases for Industry 4.0. Quality 4.0 is closely aligning quality management with Industry 4.0 to enable enterprise efficiencies, performance, innovation and business models. However, much of the market isn’t focusing on Quality 4.0, since many quality teams are still trying to solve yesterday’s problems: inefficiency caused by fragmented systems, manual metrics calculations, quality teams independently performing quality work with minimal cross-functional own
This paper will outline the value and methods involved in data mining across both quantitative and qualitative data. In addition, it will describe the data transformations necessary before doing such work, and the tools that are particularly valuable for mining mixed data types.
Enterprise networks are going through massive change, and the convergence of voice and data including Communication deployments, upgrades, transformations and ongoing management remain a top priority. According industry analysts at Aberdeen Group, 61% are planning for Communications in the next two years. In order to get the most from their Unified Communications investment at the time of deployment and beyond, enterprises must leverage both active testing and configuration analysis techniques to certify and validate that environments are configured as designed.
Published By: Dell EMC
Published Date: Feb 23, 2017
Enterprises of all sizes are undergoing massive data center modernization initiatives. They are looking to use technology to cut costs and drive competitive advantage. At the core of many of these transformations is an effort to reengineer IT delivery models to enable cloudlike flexibility of resources, and reduce both capital expenditures and operational overhead. That is giving rise to the private cloud movement. Indeed, private cloud adoption is accelerating and maturing. IT organizations are looking to the private cloud for the elasticity it can provide to add or remove resources as required. In fact, ESG research reveals that more than three-quarters of organizations would classify their private cloud deployment as either an advanced internal cloud or a basic internal cloud. Those organizations that have virtualized at least half of their production applications are more than twice as likely as their more physical infrastructure-dependent counterparts to categorize their environme
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