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The Top Three Entangled Trends in Data Architectures: Data Mesh, Data Fabric, and Hybrid Architectures

Cloudera

Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem. Data teams are confused as to whether they should get on the bandwagon of just one of these trends or pick a combination. First, we describe how data mesh and data fabric could be related.

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Huawei unveils four strategic directions for the future of finance

CIO Business Intelligence

“We use the peer-to-peer computing of CPUs, GPUs, and NPUs to enable the multi-active data center to run efficiently, just like a computer,” Mr. Cao told the conference. The distributed architecture now outperforms the original core host platform, notably with lower latency. Huawei’s new Data Intelligence Solution 3.0

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Building resilient infrastructure: the key to cloud-native, real-time decision-making

CIO Business Intelligence

“We use the peer-to-peer computing of CPUs, GPUs, and NPUs to enable the multi-active data center to run efficiently, just like a computer,” Mr. Cao told the conference. The distributed architecture now outperforms the original core host platform, notably with lower latency. Huawei’s new Data Intelligence Solution 3.0

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Power analytics as a service capabilities using Amazon Redshift

AWS Big Data

The workflow includes the following steps: The AaaS provider pulls data from customer data sources like operational databases, files, and APIs, and ingests them into the Redshift data warehouse hosted in their account. Data processing jobs enrich the data in Amazon Redshift.

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Public or On-Prem? Telco giants are optimizing the network with the Hybrid Cloud

Cloudera

The telecommunications industry continues to develop hybrid data architectures to support data workload virtualization and cloud migration. Telco organizations are planning to move towards hybrid multi-cloud to manage data better and support their workforces in the near future. 2- AI capability drives data monetization.

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Large Language Models and Data Management

Ontotext

I did some research because I wanted to create a basic framework on the intersection between large language models (LLM) and data management. But there are also a host of other issues (and cautions) to take into consideration. Another concern relates to the definition of ‘data constraints.’

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Through the Looking Glass: Suspending Judgement on Synthetic Data

TDAN

In fact, according to Gartner, “60 percent of the data used for the development of AI and analytics projects will be synthetically generated.”[1] 1] I had never heard about synthetic data until I listened to the AI Today podcast, hosted by Kathleen Welch […].