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96 Percent of Businesses Can’t Be Wrong: How Hybrid Cloud Came to Dominate the Data Sector

Cloudera

Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. The hybrid cloud’s premise—two data architectures fused together—gives companies options to leverage those solutions and to address decision-making criteria, on a case-by-case basis. .

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New Practices in Data Governance and Data Fabric for Telecommunications

Cloudera

With the volumes of data in telco accelerating with the rapid advancement of 5G and IoT, the time is now to modernize the data architecture. . for machine learning), and other enterprise policies.

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4 paths to sustainable AI

CIO Business Intelligence

The size of the data sets is limited by business concerns. Use renewable energy Hosting AI operations at a data center that uses renewable power is a straightforward path to reduce carbon emissions, but it’s not without tradeoffs. Always ask if AI/ML is right for your workload,” recommends AWS in its sustainability guidelines.

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Digital Twin at a Glance – Revolutionizing the Industrial Domain

TDAN

Prominent entities across a myriad of sectors are preparing for the digital revolution by integrating a host of technologies such as IoT, AI, Big Data, digital twins, and robotics, in their processes, products, and workflows. The industrial landscape is undergoing a digital transformation at a breakneck speed.

IoT 52
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National Grid’s energy transformation is fueled by IT

CIO Business Intelligence

Modernizing a utility’s data architecture. These capabilities allow us to reduce business risk as we move off of our monolithic, on-premise environments and provide cloud resiliency and scale,” the CIO says, noting National Grid also has a major data center consolidation under way as it moves more data to the cloud.

IT 100
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Understanding Digital Interactions in Real-Time

CIO Business Intelligence

But this glittering prize might cause some organizations to overlook something significantly more important: constructing the kind of event-driven data architecture that supports robust real-time analytics. It’s no surprise that the event-based paradigm has had a big impact on what today’s software architectures look like.

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Deciphering the Pros & Cons of Real-Time Data Streaming

Smart Data Collective

It is essential to process sensitive data only after acquiring a thorough knowledge of a stream processing architecture. The data architecture assimilates and processes sizable volumes of streaming data from different data sources. This very architecture ingests data right away while it is getting generated.

IoT 109