Building a scalable and centralized Analytics Platform for Norway’s largest bank

A walkthrough of how we are building our in-house Analytics Platform on the Cloud. We will discuss the normal daily workflows of data science users in our company and the development of our platform capabilities to facilitate their data science projects

• Common data science workflow
• Core components of the analytics platform
• Tech and architectural decisions 
• Lessons learned from user’s experience

————————————————————————————————————————————————————

Trung-Duy Nguyen – Data Engineer | DNB Bank ASA

Trung-Duy specializes in Data Engineering and Machine Learning. He has several years of professional experience in building scalable Big Data & Analytics systems. He is currently a Data Engineer in the Data & Analytics Platform team at DNB, his daily jobs are to maintain and enhance the reliability of data science workflows (data pipelines, data science workbench, productionizing machine learning models …) for hundreds of analytics users across teams and departments inside the bank. Besides that, he loves to learn and share best practices in the data world, this is why he also participated in building up the internal Data Engineering/Science community in his workplace.

Benjamin Tapley – Software Engineer | DNB Bank ASA

Ben has a PhD in Applied Mathematics from the Norwegian University of Science and Technology specialising in geometric numerical integration of differential equations. Since graduating in 2021, he has been working as a software engineer at DNB in the analytics platform where he works with various technologies related to data and analytics, such as ML pipelines, cloud computing, database management, visualisations, MLOps practices etc.