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Microservices: A Conceptual Overview

Microservices architecture allows applications to be built as a collection of independent services. Learn what microservices are used for, its benefits, and how to implement them.

What Are Microservices?

microservices glossary graphic

Microservices refer to an architectural approach where software applications are composed of small, independently deployable services that communicate with each other over a network. This architecture aims to promote flexibility, scalability, and ease of maintenance.

Built by the original creators of Apache Kafka, Confluent powers real-time, event-driven microservices communication, solving the challenges with microservices architectures to enable modern use cases.

Comparing Monolith vs Microservices

In a monolithic architecture, all components of a software application are tightly coupled and deployed as a single unit, making it difficult to make changes or updates in a nimble fashion. In contrast, a microservices architecture breaks down the application into small, independently deployable services that communicate with each other over a network, allowing for greater flexibility and scalability.

Microservices enable faster deployment of changes, easier management of complex systems, and improved fault tolerance, making it a common choice for modern, rapidly-evolving software applications.

Why Microservices? Benefits

Microservices Architecture has grown popular in recent years as enterprises look to become more agile and move towards DevOps and continuous testing.

The benefits of microservices architectures include:

Modular design for flexibility, easier maintenance, and scaling of individual services.

Independent development, testing, and deployment, leading to faster time-to-market.

Fault isolation and resiliency, allowing for failure in one service without affecting others.

Improved technology diversity, enabling the use of different programming languages and tools for different services enabling developers to use the most appropriate tools for the specific microservice tasks.

Improved alignment with modern development practices, such as DevOps and continuous delivery.

Organizational alignment: organizations within a business can provide microservices which other organizations can consume to improve their own capabilities without having to tightly coordinate with them.


Microservice architectures are not an application development panacea. Like every development methodology, they can suffer from distinct disadvantages which make it important to adopt them for the right reasons and use the right techniques to build them. A case study from the Amazon Prime Video team went viral for highlighting the benefits of switching away from a microservices architecture in favor of a monolithic architecture for their specific application.

Challenges of microservices can include, but are not limited to:

  • Dependencies between microservices: Microservices that are not sufficiently decoupled can suffer from dependencies between each other such that problems, changes or outages in one microservice produces failures in the other microservice.
  • Synchronous communication between microservices: Microservices that have to communicate with each other synchronously in order to perform their function have to be developed in tandem with each other so that they can continue to function. This undermines the supposed design of an application as a composition of loosely coupled microservices.
  • Unnecessary decentralization: If an organization embarks on a microservices architecture to build what amounts to a single application is not reaping the benefits of this architecture and is likely suffering from the drawbacks.

Microservices architectures are best considered by federated organizations where decentralized development is already in practice and is unlikely to change due to organizational or business concerns.

Microservices and Data Mesh

Data can be moved within your organization as highly scalable distributed materialized views. Since topics can be easily exposed without impacting how producing microservices behave, organizations can offer data associated with microservices as a service. When this practice is adopted organization-wide, this is called a data mesh.

Real-Time Decoupled Microservices Communication between Microservices via Apache Kafka

Apache Kafka provides real-time but decoupled communication between consumer and producer, which solves some of the problems faced by microservices architectures. Microservices that communicate directly with each other introduce unwanted and cumbersome dependencies between microservices which hinder the composition of applications. Instead, they can consume and produce independently from each other by producing and consuming to Apache Kafka instead. Because Apache Kafka is durable and highly available with exactly once guarantees, it’s a fantastic intermediary layer between microservices.

Microservices can be written to trigger when events are published to a Kafka topic by another microservice. This loosely coupled paradigm enables event-driven microservices, increasing the performance and latency of the overall application.

How Confluent’s Data Streaming Platform Helps

Confluent enables and extends all the microservice core principles. The primary functions of these technologies are well suited for microservices, including decoupling, separation of concerns, agility, and real-time streaming of event data. Developers and operators can use their preferred tools to deploy microservices since Apache Kafka imposes no precomposed opinion in the code, build and deployment toolchain.

Because the platform is resilient and fault tolerant, no batches need to be relaunched as events are simply processed (or reprocessed) in the event of a failure. High and abnormal traffic will be managed with back pressure powered by Kafka. Consumers will continue processing events as fast as they can without being overflowed by requests.

These event-driven capabilities, when put to use in the service of a microservices architecture, allow businesses to be more productive and application development to be more agile by removing dependencies and impedences between disparate groups in an organization who work with the same data.

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