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Mastering Multi-Cloud with Cloudera: Strategic Data & AI Deployments Across Clouds

Cloudera

In todays dynamic digital landscape, multi-cloud strategies have become vital for organizations aiming to leverage the best of both cloud and on-premises environments. As enterprises navigate complex data-driven transformations, hybrid and multi-cloud models offer unmatched flexibility and resilience.

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The 10 Essential SaaS Trends You Should Watch Out For In 2020

datapine

“Software as a service” (SaaS) is becoming an increasingly viable choice for organizations looking for the accessibility and versatility of software solutions and online data analysis tools without the need to rely on installing and running applications on their own computer systems and data centers. Dispelling 3 Common SaaS Myths.

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Cloudera and Snowflake Partner to Deliver the Most Comprehensive Open Data Lakehouse

Cloudera

In August, we wrote about how in a future where distributed data architectures are inevitable, unifying and managing operational and business metadata is critical to successfully maximizing the value of data, analytics, and AI.

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Why Cloud Data Governance is Critical: 9 Key Principles

Alation

Today’s organizations are rapidly embracing the cloud. This is mostly due to cost-saving and data sharing benefits. As IT leaders oversee migration, it’s critical they do not overlook data governance. Data governance is essential because it ensures people can access useful, high-quality data.

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Generative AI in the Enterprise

O'Reilly on Data

AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Even with cloud-based foundation models like GPT-4, which eliminate the need to develop your own model or provide your own infrastructure, fine-tuning a model for any particular use case is still a major undertaking.

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UK Government: From cloud first to cloud appropriate?

Cloudera

Since 2013 the UK Government’s flagship ‘Cloud First’ policy has been at the forefront of enabling departments to shed their legacy IT architecture in order to meaningfully embrace digital transformation. Combined they have the potential to create something of a data storm. . Why does this matter and why now?

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The Ultimate Guide to Modern Data Quality Management (DQM) For An Effective Data Quality Control Driven by The Right Metrics

datapine

1) What Is Data Quality Management? 2) Why Do You Need DQM? 4) Data Quality Best Practices. 5) How Do You Measure Data Quality? 6) Data Quality Metrics Examples. 7) Data Quality Control: Use Case. 8) The Consequences Of Bad Data Quality. 9) 3 Sources Of Low-Quality Data. Table of Contents.