A significant part of breaking a monolithic application into microservices involves a redesign of how the application manages data — usually through a data management strategy called “information hiding.” In this respect, breaking a monolith into microservices isn’t just about establishing clear separations between the code for different services. It’s also about creating clear separations between the data that each microservice interacts with. Why information hiding?

Most individuals who work in technology — and in particular, cloud computing — are likely aware of how service-oriented architecture (SOA) and microservices work. There is much discussion, however, regarding the best approaches for various situations. There are crucial differences in data governance, component sharing, and the architecture between SOA vs. microservices. In this article, you'll learn the basics of SOA vs.

The demand for digital transformation has accelerated, with 62% of technology leaders sharing that they fear they are at risk of being displaced by competitors who innovate more quickly. Enterprises are increasingly transitioning from monolithic to microservices architecture, with the goal to accelerate application development, speed up innovation and reduce time to market.

As we've discussed in our previous Service Discovery post, decoupled services in a microservice architecture communicate via APIs. But what about the communication between clients outside of your system and the services within your application? How does that communication work? An API gateway is a powerful component in a microservice architecture. Pairing its functionality with a serverless platform like Koyeb saves engineering teams time and maximizes computing resources efficiency.

Whether you're transitioning away from a monolith or building a green-field app, opting for a microservice architecture brings many benefits as well as certain challenges. These challenges include namely managing the network and maintaining observability in the microservice architecture. Enter the service mesh, a valuable component of modern cloud-native applications that handles inter-service communication and offers a solution to network management and microservice architecture visibility.

Microservices are a hot topic in the tech world and with good reason. These tiny systems form the backbone of a composable enterprise and most digital transformation strategies. They provide unmatched flexibility to businesses previously bound by monolithic application suites. But they present potential downsides when it comes to data management. In this article we’ll explore in more detail the relationship between microservices and master data management.

In previous posts, we covered the basics of a C++ Microservices deployment including: With those basics in place, this blog will focus on optimization of the container in a C++ Microservices deployment. We'll examine how to structure the Dockerfile and the resulting Docker image to reduce the number of layers and disk space used.

Whether you're breaking up a monolith or building a green-field application, you may consider using a microservice architecture. Like all app architectures, this model brings opportunities and challenges that a developer must be aware of in order to make the most of this app design. One such challenge is ensuring communication between your microservices.

Many companies are leveraging DevOps, microservices, automation, self-service, cloud and CI/CD pipelines. These megatrends are changing how companies are building and running software. One thing that often slips through the cracks is security. With microservices, there’s an increase in the number of APIs companies have to protect. YouTube An error occurred. Try watching this video on www.youtube.com, or enable JavaScript if it is disabled in your browser.

In a previous post, C++ Microservices in Docker, we worked through the steps for creating a docker container that exposes a HydraExpress servlet container. We successfully deployed our HydraExpress server instance in Docker, however all that was available were the default example servlets. User application code wasn’t exposed. Let’s fix that and look at deploying custom C++ Servlet instances within the HydraExpress Docker container.