When Microservices Shine
Implemented correctly, microservices architectures deliver flexibility and capabilities well beyond more monolithic approaches. “Microservices architectures deliver greater flexibility to respond to rapid market innovations such as generative AI and the need to meet consumer demand for a personalized customer experience through creative new use cases,” said Zisk.
The cloud plays a key role here, as “data cloud technology fulfills the need for a flexible architecture that allows organizations to assemble a tech stack composed of different best-of-breed technology solutions,” Zisk continued. “These are brought together through APIs to operate as a whole in a cloud-based open ecosystem. A composable microservices architecture in a data cloud provides the flexibility needed to drive an innovative CX while maintaining a single customer view.”
This advantage played out in a recruiting agency that switched to microservices to better manage numerous human resources activities independently, said Phil Strazzulla, founder of Select Software Reviews. “This improvement enabled [the agency] to upgrade and scale certain services without affecting the overall system, resulting in increased operational efficiency and reduced downtime.”
Such a microservices architecture “can dramatically improve data assets and data-driven applications,” Strazzulla continued. “Organizations may speed up data retrieval, enhance real-time analytics, and enable more flexible data governance by breaking down monolithic databases into smaller, manageable components. This modular design enables targeted upgrades and scaling, which improve performance and resource utilization.”
Another client of Strazzulla’s was able to employ microservices “to isolate its candidate tracking, payroll, and employee engagement modules,” he explained. “This division enabled them to respond faster to market demands and give more targeted analytics to their clients, resulting in richer insights and more responsive tools.”
Managing for Microservice Performance
Microservices proponents agree that attentive management is essential to microservices performance. “With a microservices architecture that allows you to connect to any best-of-breed components to drive innovation in a flexible manner, there is sometimes a tendency to compose your technology stack at too low a level,” Zisk cautioned. “What happens then is the creation of more data siloes, multiple user interfaces, and, invariably, more work for business users and IT managers who must find a way to tame the complexity.”
Zisk urged developers and architects to “designate a fully integrated platform as the center of your technology stack and wrapping self-contained business functions within the platform as composable services. What this does is center business processes in a single environment vs. having to navigate to different systems.”
A well-functioning microservices architecture “must include infrastructure, data management, and the business apps delivered,” said Zisk. “Standardize on services orchestration, management, and monitoring tools that support CI/CD development and well-governed DevOps.”
From a data management perspective, technology teams need to adopt databases that “ideally support both native SQL and federation or data lake capabilities for other kinds of data,” Zisk added. “But perhaps more important is a data modeling architecture with a well-defined set of entities, like customer, product, transaction, that represent the organization’s data needs.”
Elliot recommended employing a variety of tools to create and maintain a microservices architecture. “Containerization tools such as Docker have been critical to us, offering a consistent environment for development, testing, and production,” he said. “This uniformity facilitates deployment and promotes scalability.”
Look to Kubernetes as an orchestration platform “that manages container lifecycles and service discovery,” Elliott added. “They simplify the management of complicated microservices settings by automating deployments, scaling, and operations.”
In addition, “API management technologies provide secure and effective communication between services,” said Elliott. “We use API gateways to handle authentication, rate limitation, and logging. These tools help to keep our microservices running smoothly and securely.”
Automation, and soon, AI, will guide future microservices development and implementations, as predicted in an analysis published by IEEE and conducted by Victor Velepucha and Pamela Flores. “[I]t is important that microservices are created using domain-driven design, so that can be easily replaceable, that hide the details of their implementation, and their use is only through smart endpoints, that each microservice has one and only one business responsibility, they are formed,” they stated.
Velepucha and Flores also recommended “that both the compilation and deployment of the microservices created to be done in an automated way, and also when the microservices are created, they must have logs in such a way that through monitoring tools, the correct operation of the microservices can be reviewed.”
The future of microservices in relation to AI holds significant potential, they maintained. “Microservices that are AI-enabled can use AI’s capabilities to improve their functionality. To enable intelligent decision-making, predictive analytics, natural language processing, computer vision, and other AI-driven tasks, AI algorithms can be integrated into microservices. Microservices may have access to cutting-edge capabilities through this integration, enabling them to offer services that are intelligent and adaptive.”
For business applications, of course, “the focus should be on the means to deliver business agility, not IT agility,” Zisk advised. “So, providing a consistent user interface with an encapsulated business process will allow business users to adapt microservices to their process needs instead of the other way around.”