With the Oil and Gas Industry facing some unprecedented times and challenges with the price of crude oil, a slowing economy, and a forecasted decrease in the global market demand, there is a greater focus on a margin-based than production-based business. To address these constant challenges, new ways of thinking must be adopted to improve operational efficiency.
People intuitively know that self-driving or autonomous cars present complex engineering challenges. Vehicle assembly is the easy part – we’ve been doing that for 100 years. The real challenge is a data challenge, acquiring and managing the data needed to run the vehicles’ brain, eyes, and ears. Autonomous driving technology complexity lies in the ability to ingest, store, analyze, and deploy large volumes of data & the high bandwidth needs of data-in-motion.
Today’s globalized organizations demand a new standard for communicating and sharing information. That includes data-rich content that moves through environments, networks, and locales. From being stored, analyzed, and shared, to quickly and effectively moving between environments, to spinning up in clusters and informing endless applications—data is more critical than ever.
This blog post is part of a series on Cloudera’s Operational Database (OpDB) in CDP. Each post goes into more details about new features and capabilities. Start from the beginning of the series with, Operational Database in CDP. This blog post gives you an overview of the languages, frameworks, and applications supported by Cloudera’s OpDB.
Manufacturing has historically been laggards in its adoption of emerging technologies as business processes, from the production line to back-office operations, have inherently been tied to legacy applications. But that is rapidly changing. Faced with competitive pressures, and driven by technological enhancements across broad sectors of the economy, today’s manufacturing leaders are seeking new ways to improve productivity, reduce downtime, and streamline operations and the supply chain.
Apache YuniKorn (Incubating) is a standalone resource scheduler that aims to bring advanced scheduling capabilities for Big Data workloads onto containerized platforms. Please read YuniKorn: a universal resources scheduler to learn about the rationale and architecture. Since the time of our last post, we are delighted to update that YuniKorn was accepted by the Apache incubator in Jan 2020!
It seems everyone is talking about machine learning (ML) these days — and ML’s use in products and services we consume everyday continues to be increasingly ubiquitous. But for many enterprise organizations, the promise of embedding ML models across the business and scaling use cases remains elusive. So what about ML makes it difficult for enterprises to adopt at scale?
Too often, companies are finding out after the fact that customers have stopped using their product or service, without enough notice to have done anything about it. The term customer churn is used to describe the loss of existing customers. These are people or organizations that were using a company’s products and/or services and have decided not to use them anymore, in favor of a competitor. Tracking customer churn is a key business metric for most companies.
With the announcement of the availability of Cloudera Data Platform (CDP), our customers have been buzzing with excitement. While each one of you has been trying out different aspects of CDP, we do recognize that you are in various stages of maturity in terms of your current product adoption or implementation. Particularly, our Cloudera DataFlow customers are in various stages of product adoption.
When speaking with customers, I often hear that they are committed to digital transformation and being a data-driven enterprise. Those may just seem like abstract, lofty words to aspire to but the reality is much more practical. We have major banks needing to ensure that they have a complete view of their customers, and can reduce churn through personalized service and offerings. Telecommunications giants that absolutely need to maintain network health so there are no dropped calls or missed messages.
