Enterprise IT leaders across industries are tasked with preparing their organizations for the technologies of the future – which is no simple task. With the use of AI exploding, Cloudera, in partnership with Researchscape, surveyed 600 IT leaders who work at companies with over 1,000 employees in the U.S., EMEA and APAC regions.

Cloudera’s data-in-motion architecture is a comprehensive set of scalable, modular, re-composable capabilities that help organizations deliver smart automation and real-time data products with maximum efficiency while remaining agile to meet changing business needs. In this blog, we will examine the “why” behind streaming data and review some high-level guidelines for how organizations should build their data-in-motion architecture of the future.

Cloudera recently unveiled the latest version of Cloudera Private Cloud Base with the Zero Downtime Upgrade (ZDU) feature to enhance your user experience. The goal of ZDU is to make upgrades simpler for you and your stakeholders by increasing the availability of Cloudera’s services.

There’s nothing worse than wasting money on unnecessary costs. In on-premises data estates, these costs appear as wasted person-hours waiting for inefficient analytics to complete, or troubleshooting jobs that have failed to execute as expected, or at all. They manifest as idle hardware waiting for urgent workloads to come in, ensuring sufficient spare capacity to run them amidst noisy neighbors and resource-hungry, lower-priority workloads.

In today’s data-driven world, businesses rely heavily on their dashboards to make informed decisions. However, traditional dashboards often lack the intuitive interface needed to truly harness the power of data. But what if you could simply talk to your data and get instant insights? In the latest version of Cloudera Data Visualization, we’re introducing a new AI visual that helps users leverage the power of Large Language Models (LLMs) to “talk” to their data..

In the fast-evolving landscape of data science and machine learning, efficiency is not just desirable—it’s essential. Imagine a world where every data practitioner, from seasoned data scientists to budding developers, has an intelligent assistant at their fingertips. This assistant doesn’t just automate mundane tasks but understands the intricacies of your workflows, anticipates your needs, and dramatically enhances your productivity at every turn.

The world of Generative AI (GenAI) is rapidly evolving, with a wide array of models available for businesses to leverage. These models can be broadly categorized into two types: closed-source (proprietary) and open-source models. Closed-source models, such as OpenAI’s GPT-4o, Anthropic’s Claude 3, or Google’s Gemini 1.5 Pro, are developed and maintained by private and public companies.

At a time when artificial intelligence (AI) and tools like generative AI (GenAI) and large language models (LLMs) have exploded in popularity, getting the most out of organizational data is critical to driving business value and carving out a competitive market advantage. To reach that goal, more businesses are turning toward hybrid cloud infrastructure – with data on-premises, in the cloud, or both – as a means to tap into valuable data.

Hadoop. The first time that I really became familiar with this term was at Hadoop World in New York City some ten or so years ago. There were thousands of attendees at the event – lining up for book signings and meetings with recruiters to fill the endless job openings for developers experienced with MapReduce and managing Big Data. This was the gold rush of the 21st century, except the gold was data.

Of all of the emerging tech of the last two decades, artificial intelligence (AI) is tipping the hype scale, causing organizations from all industries to rethink their digital transformation initiatives asking where it fits in. In Financial Services, the projected numbers are staggering. According to a recent McKinsey & Co.