What are the key architectural patterns for faster time-to-market, scalability, and agility in modern business application development?

Have you ever wondered why many business products (or applications) don't scale or why it takes so long to release updates, even for minor changes? Or maybe the POC was great, but it wasn't feasible in production. Ultimately, it boils down to selecting the right architectural pattern that allows for an efficient, scalable solution to be developed and maintained.

In the course of my blog article, let me share 5 key architectural patterns with their application.

1. Microservices Architecture

Microservices, or microservice architecture, is an approach to application development in which a large application is built from modular components or services. Each module supports a specific task or business goal and uses a simple, well-defined interface, such as an application programming interface (API), to communicate with other sets of services.

In the modern era, microservices are viewed as a way to elevate companies' API-first development over code-first development and are one of the most common architectural patterns out there. Since the emergence of MicroServices architectures in recent years, they have gained tremendous traction and dominance.

Decompose complex applications into smaller, independent services to enable greater agility, scalability, and resilience.

When to use this architectural pattern?

Microservice architecture offers greater flexibility and agility compared to traditional monolithic architecture, where all functionalities of an application are tightly coupled, allowing organizations to respond quickly to changing business requirements.

A microservice's beauty lies in its polyglot nature. In other words, developers use more than one programming language or technology at the same time. Each microservice can therefore use the technology that best suits its use case.

Here are a few common microservices patterns:

a. Database-per-service Pattern: The database-per-service pattern is a microservices architecture pattern where each microservice has its own dedicated database. In this pattern, each microservice is responsible for storing and managing its own data and communicates with other microservices through APIs.

b. API Gateway Pattern: In this pattern, an API gateway acts as the entry point for all client requests. The gateway handles authentication, routing, and load balancing to distribute requests to the appropriate microservices.

c. Service Discovery Pattern: In this pattern, microservices register with a central service registry, which maintains a list of available services and their locations. Clients can use the registry to discover and communicate with the services they need.

d. Circuit Breaker Pattern: This pattern is used to manage failures and faults within a microservices architecture. A circuit breaker detects when a service is failing and redirects the request to a fallback service until the original service recovers.

e. Saga Pattern: In this pattern, a long-running transaction is broken up into a series of smaller, independent transactions that can be rolled back if necessary. This approach allows for more fine-grained transaction control and can help avoid data inconsistencies.

f. Event-Driven Architecture Pattern: In this pattern, microservices communicate with each other by sending and receiving events. Events can be used to trigger actions or to update the state of other services.

Associated Patterns

(i) Monolithic architecture - Monolithic architecture is a software design pattern in which an application is built as a single, self-contained unit, with all components tightly integrated and deployed.

(ii) Decomposition patterns - Decomposition patterns are a set of design patterns used in software engineering to break down complex systems into smaller, more manageable components, enabling better maintainability and scalability.

(iii) Messaging and Remote Procedure Invocation patterns - Messaging and Remote Procedure Invocation (RPI) patterns are a set of design patterns used in distributed systems to facilitate communication between components, where messaging patterns use asynchronous message passing, and RPI patterns use synchronous method invocations.

2. MVC architecture

The Model-View-Controller (MVC) architecture pattern is a software design pattern used to separate the concerns of an application into three distinct components: the model, the view, and the controller.

The Model represents the data and the business logic of the application. It is responsible for managing the application's data and the operations that can be performed on that data.

The View is responsible for presenting the data to the user. It is the user interface component of the application and is responsible for displaying the data in a way that is understandable and visually appealing.

The Controller acts as an intermediary between the Model and the View. It receives input from the user and then interacts with the Model to perform the necessary operations. Once the operations are complete, the Controller updates the View with the new data.

When to use this architectural pattern?

MVC is commonly used in web application development but can also be applied to other types of software applications. It is a widely adopted architecture pattern and is supported by many popular programming languages and frameworks.

MVC offers several benefits, including improved concern separation, maintainability, and modularity. Separate components make updating and modifying an application easier without affecting other components.

Associated Patterns

(i) Model-View-Presenter (MVP) -? The Model View Presenter (MVP) is a design paradigm, which is architecturally the derivation of the MVC pattern, and is generally used for building user interfaces.

The View and Presenter relationship is one-to-one. MVP is a popular design pattern adopted in android programming where the presenter becomes the middleman containing UI business logic for the View.

(ii) Model-View-ViewModel (MVVM) -? Using this architectural pattern, the interface logic is moved away from the View in an MVC so that the view has direct access to the model state or transforms/logic for the model state, which resolves the uneven scattering of functionality in an MVC.

The ModelView is not aware of the View. The View to ViewModel relationship is many-to-one. Everything except the UI View and the Business/Data Model is part of the ViewModel.

(iii) Model-View-Adapter (MVA) - Model-View-Adapter (MVA) is an architectural pattern that is also known as the “mediating controller MVC” or MVC with the mediator pattern applied.

This pattern has all traffic pass through a “mediator, " the Adapter in this case.

(iv) Model-View-Intent (MVI) - MVI is a reactive Architecture Pattern that is short for Model-View-Intent. It introduces two new concepts: the intent and the state. UI might have different states — Loading State, Fetch Data State, Error State, User button submit state, etc.

Model-View-Intent (MVI) is a software architecture pattern that separates an application's data, user interface, and business logic into distinct components, where the intent serves as a unidirectional communication channel between the view and the model.

3. Infused Analytics Architectural Pattern (NLP) in applications

Infused analytics is the integration of analytics and business intelligence (BI) capabilities directly into business applications, workflows, and processes. This means that instead of requiring users to access a separate analytics tool or platform, insights, interactive reports and dashboards, and curated data are presented directly within the context of the application, allowing users to make data-driven decisions and take immediate action.

“Search-driven analytics is often described as 'Google for numbers' and can encourage adoption by users intimidated or resistant to conventional interfaces for interacting with data and traditional overwhelming dashboards.”

When to use this architectural pattern?

In the case of 'search-driven' analytics being required as part of the analytics, this will encapsulate the complexity of the analytics layer as part of the application. Infused analytics can take many forms, including embedded dashboards, data visualizations, and predictive analytics.

(a) Find answers to any business question- With its intuitive, Google-like search interface, users can quickly find insights and answers to their business questions without knowing SQL or having deep technical expertise. ?By providing a more accessible way to interact with data, it can also help to democratize data access across an organization, empowering more people to use data to drive business outcomes.

(b) Visualize - The embedded NLP capabilities of infused analytics data offer real-time visualization of business metrics through interactive 'liveboards.' The Liveboards allow users to customize their dashboard view by drilling down into the data, adding measures, applying filters, and presenting the data in real time.

(c) Monitor - Businesses can identify issues or opportunities in real-time when KPIs are monitored in real-time. As a result of gaining insight into why changes are occurring, businesses can take more informed and targeted action to resolve issues or capitalize on opportunities.

4. Serverless Architectural Pattern

Serverless architecture is implemented in applications through the use of a cloud provider's serverless computing platform, such as AWS Lambda, Azure Functions, or Google Cloud Functions. These platforms allow developers to write code in various programming languages (such as JavaScript, Python, .NET, or Java) and deploy it as a function that can be executed in response to events or requests.

"Focus on your code, not on managing servers."

Common serverless architecture patterns include:

• Serverless APIs, mobile and web backends.
• Event and stream processing, Internet of Things (IoT) data processing, big data, and ML pipelines.
• Automation, digital transformation, and process automation.
• Software-as-a-Service (SaaS), Middleware, and big data projects.
• Integration and enterprise service bus to connect line-of-business systems, publish and subscribe (Pub/Sub) to business events.

When to use this architectural pattern?

The implementation of serverless architecture typically involves breaking an application down into smaller, independent functions that perform specific tasks. These functions are then deployed to the serverless computing platform, which automatically manages the allocation of resources needed to run the functions.

When a request or event is received, the serverless computing platform automatically triggers the appropriate function and executes it in a containerized environment. The platform automatically scales up or down the number of containers as needed to handle incoming requests or events and then shuts down the containers when they are no longer required.

Due to the fact that cloud service providers only charge for the amount of computation used with serverless, it can be cost-effective in most cases.

Associated Patterns

(i) Container-as-a-Service - In serverless architecture, Container-as-a-Service (CaaS) enables developers to easily deploy and manage containerized applications as portable and lightweight units, abstracting away the complexity of the underlying infrastructure.

(ii) Function-as-a-Service - Function-as-a-Service (FaaS) in serverless architecture is a cloud computing model where developers can deploy and execute code as event-triggered functions without having to worry about managing the underlying infrastructure.

(iii) Event-driven architecture - Event-driven architecture (EDA) in serverless architecture is an approach where computing resources and software components are triggered by events, enabling efficient and sc

5. Federated ML Training Model - Architectural pattern

The Federated ML learning technique allows multiple parties to train a shared model collaboratively without sharing the underlying data. Federated Learning is akin to decentralized machine learning training. The training data in federated learning is distributed across multiple devices, and the model is trained locally on each device. A global model can then be created by aggregating the local models that have been trained in order to create a model that can be used for inference.

"Collaboratively train machine learning models on distributed data sources while preserving privacy and security, enabling efficient and scalable model development for various industries."

When to use this federated ML training model?

Federated learning can be suitable for applications where a machine learning model needs to be trained on large amounts of data distributed across many devices or nodes while requiring data distribution, scalability, diverse data, collaborative training, and preserving the privacy of the data.

Associated Patterns

(i) Deep Neural Networks - Deep Neural Networks (DNNs) are a class of machine learning models composed of multiple layers of interconnected neurons designed to extract and process complex features from input data.

(ii) Recurrent Neural Networks - Recurrent Neural Networks (RNNs) are a type of neural network that can handle sequential data by retaining information through feedback connections between their neuron layers.

(iii) Convolutional Neural Networks - Convolutional Neural Networks (CNNs) are deep neural networks that excel at image processing and recognition tasks using specialized layers for feature extraction and transformation.

(iv) Generative Adversarial Networks (GANs) - Generative Adversarial Networks (GANs) are a type of deep learning model that generates synthetic data by training a generator network to produce realistic samples. In contrast, a discriminator network learns to distinguish between real and fake samples.

(v) Generative Pretrained Transformer (GPT) language model - Generative Pretrained Transformer Language Models (GPTs) are a type of language model that uses a transformer architecture and pretraining on vast amounts of text data to generate human-like text with high coherence and fluency.