In this episode we interview Tiago Rodrigues Antao, to learn more about how and why Python has taken the data processing world by storm. We will also learn some of the language's limitations, examples of how to address them, and how these could help minimize the commercial costs of your application and potentially your code's effect on the environment. You can learn more about this topic in his new book High-Performance Python for Data Analytics.
In this epsisode, Randy Lariar, a Management Consultant at EY, talks to us about data trends, the importance of a data strategy, and its different elements. We get into concepts like data management, regulatory implications and business strategy. You can find Randy on LinkedIn https://www.linkedin.com/in/lariar/episode
In this episode, I interview David Sturzenegger, Head of Product at Decentriq. One of the biggest challenges of machine learning is having customers trust that their data is being handled safely. During this conversation, you will learn about confidential computing and how it can help develop analytics products in consumer products, healthcare, and beyond.
In this episode I talk to Tom Buoniello from Binary Edge who explains some of the complexities of cybersecurity insurance underwriting, interesting use cases for threat intelligence data and how to complement not replace your cybersecurity strategy with cybersecurity insurance.
In this episode, Jean Georges Perrin, Software Architect and IBM Champion talks to us about the benefits of Spark for data analysis. We also go into the motivation for writing his new book Spark in Action which allows developers to get the benefits of Apache Spark in Java and without having to learn the intricacies of Scala.
In this episode Thomas Kunnunpurath VP of Engineering at Solace talks to us about event-driven architectures and how to use it for application development and analytics. Being able to consume events in real-time allows for creating better User Experiences and achieving Real-Time data analysis to build more responsive data products,