The Oil and Gas industry is arguably more familiar with managing an array of changing risk factors than almost any other sector in the United States. The industry is used to facing strong headwinds and has proven itself adept at adjusting to often rapidly shifting business conditions, whether that is regulatory change, big fluctuations in supply and demand, or evolving security supply chain concerns.

Classic data modeling and history-based actuarial models do not comprehensively work anymore. In order to get useful insights that can be implemented to support customers and the business, insurers must rapidly incorporate new data sources in their analysis. Current events that are different than anything we have seen in recent history force us to get used to a new world that cannot totally be evaluated and analyzed based on historical experience and knowledge.

Guest post by Mark Ferman, Sr. Oil & Gas Analytics Advisor Oil and Gas companies operate within one of the most demanding business environments on the planet with an array of complex challenges that regularly test their ability to innovate, plan, and execute strategic objectives.

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 NoSQL, component integration, and object store support capabilities of OpDB.

While cloud providers and data analytics firms are proliferating across markets and landscapes, what distinguishes one from another? How can you know which one holds the keys to your agency’s digital transformation? The reality is that no matter how slick the advertising, how pervasive the presence across conferences and webcasts, or how high the C-suite’s former government offices … it’s the offerings that matter most.

Many enterprise data science teams are using Cloudera’s machine learning platform for model exploration and training, including the creation of deep learning models using Tensorflow, PyTorch, and more. However, training a deep learning model is often a time-consuming process, thus GPU and distributed model training approaches are employed to accelerate the training speed.

Time series data and real-time data acquisition is growing at a 50% faster rate than static, latent, or historical data. In some ways, it has become more important than any other type of data, as it provides real-time decision making, enables autonomous decisions at the edge, and allows for more complex Machine Learning (ML) applications. Time series data and real-time data acquisition dominate industrial use cases, as it is ubiquitous with the manufacturing process.

There are no silver bullets to solve your data issues in this Big Data world. This truism became repeatedly apparent in Searcher Seismic’s journey towards better data management. We realized that every choice has compromises associated with it, and a robust solution will necessarily integrate many individual pieces of technology and business processes. Having said that, there are some solutions that are undeniably better than others.

The importance of digital banking and electronic commerce has proven all the more important during the pandemic. Online shopping is the only choice in many cases for conducting commerce. A recent McKinsey report, pre-COVID 19 outbreak, revealed that retail digital banking acceptance was already high. It has increased to the point where 60% of customers under the age of 70 use digital channels. That number increases to 75% for those under the age of 50.

Apache HBase became a top-level project with Apache 10 years ago and Cloudera began contributing to it at the same time (2010). Over this time, it has become one of the largest and most popular open-source tools in big data and one of the most popular NoSQL databases.

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.

For more than a decade, government CIOs have been gearing up for and championing digital transformation. And not a moment too soon: From federal headquarters to statehouses, agencies today are expected to mirror the near-seamless user experience of today’s commercial sector, delivering agile, efficient responsiveness to constituent needs.

The healthcare industry is crumbling under the weight of disruption. Newly empowered patients have high expectations for procedure and price transparency, and personal health information access, to enable informed treatment choices. Providers must deliver care faster, better and within a framework of rigorous quality, compliance, and cost containment guidelines. Drug and medical device makers are under pressure to deliver critical therapies quickly while ensuring safety, efficacy, and affordability.