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.

Editor’s note: Today we’re hearing from some of the team members involved in building BigQuery over the past decade, and even before. Our thanks go to Jeremy Condit, Dan Delorey, Sudhir Hasbe, Felipe Hoffa, Chad Jennings, Jing Jing Long, Mosha Pasumansky, Tino Tereshko, and William Vambenepe, and Alicia Williams. This month, Google’s cloud data warehouse BigQuery turns 10.

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.

We all know visualization alone is not enough in the world of modern BI. And, when Qlik Sense was introduced, we focused on building a world-class platform, driven by our associative engine, open APIs and modern architecture. Our vision was to drive all the major analytics use cases, and support a lightning fast pace of innovation for the next decade and beyond.