Imagine binge-watching a favorite series on Netflix or shopping on Amazon during the peak holiday season. Like you, thousands of others use the app at the same time. Suddenly, the app stops working. It takes an eternity to load.
Sounds frustrating, doesn’t it? Perhaps you become so frustrated that you plan to use the competitor’s app to shop or watch something else on another streaming app.
Incidents like these are common. Minor inconveniences as they seem, repeated incidents like these could lead to customer churn.
That’s why large enterprises like Amazon and Netflix have invested in scalable architecture to make the user experience smooth and seamless.
What Is A Scalable Architecture And Why Does It Matter?
A scalable architecture is the app or software’s capability to scale in response to changing workloads without making any fundamental changes to the system. It allocates resources in response to the workload.
This is important because the workload differs at all times. It could peak during the holiday season and go through a lull afterward.
Thus, enterprises must be prepared to add more resources and remove them as per the demand.
A scalable architecture helps enterprises:
- Improve cost-effectiveness: Enterprises often overprovision resources to manage peak workloads, which go unused after the workload reduces. This leads to unnecessary expenses. Scalable architecture provisions resources only when there is a need. This helps them minimize costs and improve cost-effectiveness.
- Accelerate response to workload changes: Scalable architecture is designed to manage peak workloads and traffic by adding resources. This prevents issues like unexpected downtime and delay in loading apps and builds customer trust.
- Improve application’s reliability and stability: Scalable architecture distributes the workload across various resources like servers and databases. This reduces dependency on a single component and ensures business continuity despite disruptions. The architecture is built to manage large data volume and surges in usage. Thus, the chances of failure are negligible. The architecture makes the app stable and reliable.
- Reduce downtime: Failure during workload changes is inevitable, but scalable architecture minimizes the impact through fault tolerance, redundancy, load balancing, and automated scaling. These components prevent disruptions and reduce the business impact due to unexpected downtime. For example, load balancing ensures that a single server is not burdened and distributes workloads across various servers. Similarly, it can automatically adjust resources based on demand, which helps apps manage unexpected downtime due to traffic spikes and outages.
There are various ways to add resources. You can add them manually or use microservices and data mesh to scale the architecture.
In this blog, we will discuss how microservices and data mesh can go hand in hand for scalable architecture.
The Role Of Microservices and Data Mesh In Scalable Architecture
Before we deep-dive into the topic, let’s understand what microservices and data mesh do.
- Microservices: Unlike monolithic architecture, microservices architecture allows enterprises to design and develop modular, flexible, and scalable applications. The microservices architecture comprises small, loosely coupled, autonomous services.
Each of these services has a separate codebase and can be deployed independently. They are responsible for specific functions. These services communicate through RESTful APIs, message queues, and gRPC.
Microservices can play a significant role in building scalable architecture.
- To begin with, you don’t have to scale the entire application. Since each service serves a specific purpose, you can only add more resources to those services facing a traffic spike.
- You don’t have to shut down the entire application if there is a failure in one service. You just have to isolate the faulty service and address the issue. This ensures the app’s stability and reliability.
- Microservices allow you to deploy the services across multiple resources like servers. This makes it easy to add more instances to manage traffic.
- Data mesh: Data mesh is a decentralized data architecture. It democratizes data accessibility. Like microservices that decentralize services, the democratization of data allows your different teams to manage their data independently. This enables each team to analyze data and make informed decisions faster.
Data mesh can also play a role in deploying scalable architecture.
- Since data mesh decentralizes data ownership, there will be little dependency on the central data team. This enables the teams to scale data infrastructure independently without burdening a single infrastructure or team.
- The decentralized nature of data mesh is also helpful in eliminating the common bottlenecks in centralized systems and allowing individual teams to scale autonomously.
- Since data mesh is technology-agnostic, each domain can use tools and store data in data lakes or warehouses, depending on their requirements. This capability allows you to scale smoothly, irrespective of the data and technology stacks.
How To Use Microservices And Data Mesh To Scale Architecture
So, we know the individual roles of microservices and data mesh in developing scalable architecture. Let’s find out how both can work in synergy.
You see, both microservices and data mesh work on the shared principle of autonomy and decentralization. In fact, it is safe to say that data mesh works on the same principle as microservices.
While microservices decentralize services, data mesh democratizes data accessibility and ownership. This reduces dependency on a single service or a team and helps build autonomy and scale applications smoothly.
Combining both can help enterprises scale data and services without affecting other teams or services and respond faster to market demands.
Let’s understand this further by using a global e-commerce company as an example. While scaling its business, the company encountered problems like data silos and service bottlenecks. However, they soon addressed these challenges by combining microservices and data mesh and built a smooth-functioning application. While microservices helped the company manage the various functionalities like payment processing, inventory management, and customer relationship management, data mesh helped them manage the data for each domain. The combination helped the company reduce dependency on a single service or team, innovate more, and scale the business without worrying about downtime or system failure.
Conclusion
As customers demand faster services and smoother experiences, the onus lies with the enterprises to meet these expectations. That’s why enterprises need a scalable architecture that can adjust the system in response to changing workloads.
Microservices in combination with data mesh can help enterprises achieve scalable architecture goals.
Since both work on the principles of autonomy and decentralization, enterprises can quickly scale their apps and manage data without worrying about system failure or data availability.
All they need is the right partner to help them align data mesh and microservices and take their business to the next level.
Looking for a technology partner to help you build a scalable architecture with microservices and data mesh? Contact us to know more.
