Schedule: Sponsored sessions

The development of autonomous driving cars requires the handling of huge amounts of data produced by test vehicles and solving a number of critical challenges specific to the automotive industry. Miha Pelko and Aleksandr Melkonyan outline these challenges and explain how BMW is overcoming them by adapting and reinventing existing big data solutions for autonomous driving. Read more.

What was once science fiction has now become reality as multiple AI consumer-based solutions have hit the market over last few years. In turn, consumers have become more comfortable interacting with AI. But has AI really lived up to the hype? For consumers, perhaps not yet. However, AI for business is a different (and more valuable) animal. Carlo Appugliese details how business can put AI to work. Read more.

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Mathew Lodge demonstrates that it's just as easy to deploy Python as it is Java, using containers and Kubernetes. Welcome to the future. Read more.

Today, every company is a data company. Business success depends on putting large volumes of live data to work to drive competitive advantage. Paul Phillips details how some of the world’s largest companies have achieved 100% uptime while moving massive live datasets and halving their hardware requirements. Read more.

Business intelligence (BI) and analytics on data lakes have had limited success. Data lakes often fall short because they are mostly used by data scientists and not by business users. Randy Lea explains why existing BI tools work well for data warehouses but not data lakes and why modern BI tools designed for data lakes should represent the second BI standard in enterprises today. Read more.

Han Yang explains how Cisco is leveraging big data and analytics and details how the company is helping customers to incorporate data sources from the internet of things and deploy machine learning at the edge and at the enterprise. Read more.

Wael Elrifai shares his experiences working in the IoT and AI spaces, covering complexities, pitfalls, and opportunities to explain why innovation isn’t just good for business—it's a societal imperative. Read more.

Modern analytics and AI initiatives require an adaptable data lake with a multistage architectural design to effectively ingest, stage, and provision specific datasets in real time. Ted Orme discusses his experience at Attunity creating a real-time data integration solution for Fortune 100 organizations and shares best practices and lessons learned along the way. Read more.

If your company isn’t good at analytics, it’s not ready for AI. Ryan Lippert explains how the right data strategy can set you up for success in machine learning and artificial intelligence—the new ground for gaining competitive edge and creating business value. Read more.