Slowly Changing Dimensions in Data Science
Avoiding a common pitfall in data science by enabling history mode.
Avoiding a common pitfall in data science by enabling history mode.
Use schemas to make life easier for your analysts and engineers.
If each product is a world in its own, each industry in which that product -or service, for that matter- is deployed, is a universe. A seemingly chaotic universe full of data coming from every direction and angle that you, the product manager, need to catch, analyze, and funnel into your every day. If this does not sound easy, it is because it is not!
A decade ago, all developers could talk about was breaking down the monolith and event-driven architectures. Especially in the financial services industry, to become more nimble and accelerate their application delivery. They leveraged messaging systems to decouple the application, and specifically Apache Kafka has transitioned from being a data integration technology to the leading messaging system for microservices.
Microservices have become a popular way to architect applications, particularly those that compose functionality from a variety of loosely coupled systems and services. While there are a variety of frameworks and tools for implementing a microservice architecture, it isn’t always clear how to expose native code like C or C++ code within a wider microservice system. That’s where HydraExpress comes in.
Insurers, reinsurers and intermediaries are under pressure to adapt to new customer expectations. Insurtechs have made omnichannel digital experiences the norm. And COVID-19 has forced the issue further, on top of necessary operational and claims process changes. Digital transformation is no longer a buzzword or something that can fix just one area of an insurance organization.