Since the start of the pandemic nearly a year ago, there's been one word on the lips of every business leader, analyst, and investor around the world: cloud. COVID-19 fundamentally changed the way businesses operate. In response, organizations went all in on cloud, betting on the unmatched scale, speed, and security of SaaS applications to help them weather the storm. Nowhere was this shift more pronounced that in our own data and analytics industry.

What’s the fastest and easiest path towards powerful cloud-native analytics that are secure and cost-efficient? In our humble opinion, we believe that’s Cloudera Data Platform (CDP). And sure, we’re a little biased—but only because we’ve seen firsthand how CDP helps our customers realize the full benefits of public cloud.

You’ve probably heard it more than once: Machine learning (ML) can take your digital transformation to another level. It’s a pie-in-the-sky statement that sounds great, right? And while you’d be forgiven for thinking that it might sound too good to be true, operational ML is, in fact, achievable and sustainable. You can get the very kind of ML you need to increase revenue and lower costs. To help teams work smarter and do things faster.

The United States Veterans Administration (VA) over the last decade underwent a massive enterprise-wide IT transformation, eliminating its fragmented shadow IT and adopting a centralized system capable of supporting the agency’s 400,000 employees and more effectively utilizing its $240 billion-plus annual budget. The result: A more reliable and modern IT environment that improves access, availability, and user experience -ultimately supporting the VA mission more effectively.

One of the changes that we've seen happening in the analyst space recently is a huge shift in thinking. Gartner in particular is now talking about augmented consumers and multi-experience analytics. To me, this is really interesting because they’re talking about the business user and how they want to work and consume data. In the past it was all about the data analyst, but focusing on users opens up an entirely new level of thinking.

From the Wright Brothers and Ada Lovelace, to Elon Musk and Steve Jobs, when we consider who is behind the most celebrated innovations and industry transformations, we often think about individual bright thinkers and disruptors. However, over the years, studies have shown that the greatest potential lies in the “power of many," fostered by a shift in how new generations work.

When working on complex, or rigorous enterprise machine learning projects, Data Scientists and Machine Learning Engineers experience various degrees of processing lag training models at scale. While model training on small data can typically take minutes, doing the same on large volumes of data can take hours or even weeks. To overcome this, practitioners often turn to NVIDIA GPUs to accelerate machine learning and deep learning workloads.

This blog series follows the manufacturing and operations data lifecycle stages of an electric car manufacturer – typically experienced in large, data-driven manufacturing companies. The first blog introduced a mock vehicle manufacturing company, The Electric Car Company (ECC) and focused on Data Collection. The second blog dealt with creating and managing Data Enrichment pipelines. The third video in the series highlighted Reporting and Data Visualization.

Enterprise data warehouse platform owners face a number of common challenges. In this article, we look at seven challenges, explore the impacts to platform and business owners and highlight how a modern data warehouse can address them.

Many organizations are working to become more data-driven – increasing data use and leveraging data insights to improve decision-making, solve their most challenging problems and improve revenue and profitability. A February 2020 IDC survey showed a direct correlation between quality decision-making and strong data-to-insight capabilities; 57 percent of organizations with the best data analytics pipelines received the highest decision-making score.