Data Platforms – 1 Nov

Based on his experiences with dozens of organizations, from digital native to enterprise, Diederik Greveling shares what are the components of a modern cloud-based data platform. During this session. you will learn about the best cloud services to use to build an efficient platform and which additionals tools and components you can use to build a modern data platform.

The world’s best sports organizations are using AWS to build data-driven solutions and reinvent the way sports are watched, played, and managed. AWS provides cloud services that are at the core of sports innovation, athlete optimization, and epic fandom. Machine learning (ML) is producing advanced stats like NFL’s Next Gen Stats, high-performance computing (HPC) is redesigning F1 race cars and America’s Cup boats, organizations like NASCAR are using artificial intelligence (AI) services to automatically tag and categorize archives, and Internet of Things (IoT) is connecting sensors from the field and track to deliver real-time data. Whether it's predicting the probability of a catch in real-time, or forecasting ticket sales after a winning season, technology is changing the game.

Computer vision and deep learning are used at Schiphol to get insights in the processes that are needed to prepare a plane for take-off. We have cameras aimed at parked planes which are used to detect in real-time the execution of the so-called turnaround process which is often a source of delay. Using real-time alerts and insights from historical data, this solution enables Schiphol to discover issues in the turnaround process. In this interview we will discuss the value of data centric AI and how we use it to continuously improve our models.

In this session, Adevinta's senior software engineer Charlie Evans and GoDataDriven's Diederik Greveling share their experiences migrating an on-prem backend data analytics pipeline that processes over one billion events per day to Google Cloud. They will touch on the rationale behind the migration, the changes in architecture, driven in part by FinOps considerations, and discuss the pain points encountered along the way.