Remove resource-center integration-guides how-to-evaluate-a-data-catalog
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Cloud Analytics Powered by FinOps

Cloudera

The legacy IT infrastructure to run the business operations — mainly data centers — has a deadline to shift to cloud-based services. The public cloud is increasingly becoming the preferred platform to host data analytics – related projects, such as business intelligence, machine learning (ML), and AI applications. Why FinOps?

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AI adoption accelerates as enterprise PoCs show productivity gains

CIO Business Intelligence

This involves rigorous evaluation of potential benefits, risks, and costs associated with each AI initiative to ensure investments are prudent and aligned with our risk-return profile.” We need to continue to be mindful of business outcomes and apply use cases that make sense.” We want to maintain discipline and go deep.”

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Artificial intelligence and a new era of human resources

IBM Big Data Hub

Artificial intelligence (AI) can help usher in a new era of human resource management, where data analytics, machine learning and automation can work together to save people time and support higher-quality outcomes. These conversations are happening as the workforce is undergoing a major change.

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Design a data mesh on AWS that reflects the envisioned organization

AWS Big Data

The company uses AWS Cloud services to build data-driven products and scale engineering best practices. To ensure a sustainable data platform amid growth and profitability phases, their tech teams adopted a decentralized data mesh architecture. The solution Acast implemented is a data mesh, architected on AWS.

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What Is a Data Fabric and How Does a Data Catalog Support It?

Alation

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric.

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Themes and Conferences per Pacoid, Episode 13

Domino Data Lab

Paco Nathan’s latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference. Data Science meets Climate Science. Data veracity, data stewardship, and heros of data science.