Paul Bruffett is the architect responsible for Devon Energy’s big data and advanced analytics platform. With a background in distributed systems design and domain driven microservices solution architecture, Paul is currently focused on leading Devon’s migration from an on premises Hadoop cluster and datawarehouse to an agile, cloud first big data environment and data lake. Paul also uses his experience in deep learning and analytics to lead the design and implementation of a platform for developing and deploying machine learning and deep learning pipelines using modern technologies and frameworks.
April 23, 2019 05:00 PM PT
Devon Energy is a Fortune 500 company focused on unconventional upstream oil and gas production. With a companywide focus on innovation and data-driven decision making, IT has been challenged to make more data available to more people more quickly. To this end, we have leveraged the scale of Microsoft Azure and Databricks’ Unified Analytics Platform to help reimagine our integration, data warehousing and analytics landscape to improve agility while moving our workloads to the cloud.
We are in the third year of this transformation and have lessons learned around improving the testability of data pipelines, code management, model training and deployment, promotion, and user empowerment. In this talk, we will share our experience managing the lifecycle of data engineering and machine learning solutions and striking the balance between agility and reliability in a single platform, while democratizing data access to users from all disciplines across the company.
June 4, 2018 05:00 PM PT
Devon Energy has a team who continuously monitors all of its active drilling rigs and teams of engineers and completions crews. This team ensures wells, which are drilled horizontally for up to two miles, stay in the proper formation; a target that can be mere tens of feet wide. As the industry’s activity picks up, this team is increasingly stretched thin. In order to make these experts more productive, Devon Energy has developed a solution that analyzes subsurface attributes and readings in order to predict the drill’s position. The sheer number of possible interpretations for this data and the ever increasing length of well bores means that only a scale-out compute platform can solve the problem.
Our solution, built on Azure Databricks, scales to over a thousand cores in order to process a full well in one to two hours and uses graph and statistics libraries to help maximize oil and gas production.
Session hashtag: #Ent3SAIS