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.
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.
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.
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.
