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A Look Back at the Gartner Data and Analytics Summit

Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.

FedRAMP In Process Designation, A Milestone in Cybersecurity Commitment

It’s been said that the Federal Government is one of, if not the largest, producer of data in the United States, and this data is at the heart of mission delivery for agencies across the civilian to DoD spectrum. Data is critical to driving the innovation and decision-making that improves services, streamlines operations and strengthens national security.

Cloudera Named a Visionary in the Gartner MQ for Cloud DBMS

We’re excited to share that Gartner has recognized Cloudera as a Visionary among all vendors evaluated in the 2023 Gartner® Magic Quadrant™ for Cloud Database Management Systems. This recognition underscores Cloudera’s commitment to continuous customer innovation and validates our ability to foresee future data and AI trends, and our strategy in shaping the future of data management.

Data Architecture and Strategy in the AI Era

At a time when AI is exploding in popularity and finding its way into nearly every facet of business operations, data has arguably never been more valuable. More recently, that value has been made clear by the emergence of AI-powered technologies like generative AI (GenAI) and the use of Large Language Models (LLMs).

Don't Get Left Behind in the AI Race: Your Easy Starting Point is Here

The ongoing progress in Artificial Intelligence is constantly expanding the realms of possibility, revolutionizing industries and societies on a global scale. The release of LLMs surged by 136% in 2023 compared to 2022, and this upward trend is projected to continue in 2024. Today, 44% of organizations are experimenting with generative AI, with 10% having already implemented it in operational settings. Companies must act now in order to stay in the AI Race.

Cloudera's RHEL-volution: Powering the Cloud with Red Hat

As enterprise AI technologies rapidly reshape our digital environment, the foundation of your cloud infrastructure is more critical than ever. That’s why Cloudera and Red Hat, renowned for their open-source solutions, have teamed up to bring Red Hat Enterprise Linux (RHEL) to Cloudera on public cloud as the operating system for all of our public cloud platform images. Let’s dive into what this means and why it’s a game-changer for our customers.

A Closer Look at The Next Phase of Cloudera's Hybrid Data Lakehouse

Artificial Intelligence (AI) is primed to reshape the way just about every business operates. Cloudera research projected that more than one third (36%) of organizations in the U.S. are in the early stages of exploring the potential for AI implementation. But even with its rise, AI is still a struggle for some enterprises. AI, and any analytics for that matter, are only as good as the data upon which they are based. And that’s where the rub is.

Metadata Management & Data Governance with Cloudera SDX

In this article, we will walk you through the process of implementing fine grained access control for the data governance framework within the Cloudera platform. This will allow a data office to implement access policies over metadata management assets like tags or classifications, business glossaries, and data catalog entities, laying the foundation for comprehensive data access control.

Using Streams Replication Manager Prefixless Replication for Kafka Topic Aggregation

Businesses often need to aggregate topics because it is essential for organizing, simplifying, and optimizing the processing of streaming data. It enables efficient analysis, facilitates modular development, and enhances the overall effectiveness of streaming applications. For example, if there are separate clusters, and there are topics with the same purpose in the different clusters, then it is useful to aggregate the content into one topic.