Computational challenges and opportunities of astronomical big data

Bas Geerdink explains why and how ING is becoming more and more data-driven, sharing use cases, architecture, and technology choices along the way.

The rise of densely populated, highly built-up smart cities around the globe has stretched the capabilities of current 2D visualization techniques. With the advent of drones, IoT devices, and indoor geolocation, next-gen 3D visualizations are beginning to address this challenge. Victor Chua explores how SmartHub is gearing up for a 3D future to support cutting-edge data analytics.

Apache Spark ML and MLlib are hugely popular in the big data ecosystem, and Intel has been deeply involved in Spark from a very early stage. Peng Meng outlines the methodology behind Intel's work on Spark ML and MLlib optimization and shares a case study on boosting the performance of Spark MLlib ALS by 60x in JD.coms production environment.

John Mertic and Cupid Chan share real end-user perspectives from companies like GE on how they are using big data tools, challenges they face, and where they are looking to focus investmentsall from a vendor-neutral viewpoint.

Xianyan Jia and Zhenhua Wang explore deep learning applications built successfully with BigDL. They also teach you how to develop fast prototypes with BigDL's off-the-shelf deep learning toolkit and build end-to-end deep learning applications with flexibility and scalability using BigDL on Spark.

Access to credit in emerging markets is impeded by issues around identity verification, risk assessment and monitoring, and the costs of underwriting and collections. At the core of it all is a lack of data. Amit Das explains how accessing alternate data, real-time risk monitoring and data access solutions, and smart analytics is changing the lending landscape in India.