SoftElegance, System Architect
Yaroslav Nedashkovsky got Master’s degree in Applied Mathematics and System Analysis in 2008 from National Technical University of Ukraine, however made the first steps in software development around 2004. From 2004 to 2008 worked under a contract for Institute of Petroleum UAS, Kyiv, Ukraine. Started as a software engineer in projects related with data mining and visualization of seismic data. Since 2011 works in SoftElegance. He has a profound experience in building various successful SaaS solutions, data lake, mostly specialized in distributed system, IoT, and Big Data. From 2015 works as a System Architect. Big fan of machine learning.
Topic: Digitalization of Oil Industry & How Machine Learning Helps Predict Equipment Failure
At SoftEleganceData we are building unified data lake for the oil industry — it is a software architecture and a set of microservices that are used to get business values from the data that are generated during the oil production.
There were used modern capabilities of Big Data Architecture, based on machine learning, archived data, and streaming data from wells to build a unified math model to predict failure of that kind of industrial equipment.
The introduction of presentation will include architectural overview of Data Lake with short description of technologies that are used, and what is the reason for business to develop it. The main part of the presentation will show the practical example how to use Spark Streaming for data collection and preprocessing from oil rigs and than reuse it through different machine learning technics for building predictive maintenance. It would be presented the math model to predict failure of rod pumps during the oil artificial lifting. Also, it would be shown the full cycle of data flow, with the technologies that are used for each process: injection data, preprocessing, analyze, and prediction, that will be executed during data streaming.