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ScienceSoft’s Approach to Software Architecture Design
In software engineering since 1989, ScienceSoft delivers lasting value through pragmatic software architectures that strike the perfect balance between functionality, cost, and impact.
4 Cornerstones of Our Approach to Software Architecture Design
Mastering trade-offs and complex compromises
With 10–20 years of experience, our software architects excel at making nuanced trade-offs and compromises between performance, scalability, maintainability, and cost.
Prioritizing practicality over over-engineering
We apply design patterns only when they provide clear benefits, avoiding unnecessary complexity that hinders maintainability and evolution.
Selecting trustworthy technologies and tools
Avoiding shiny object syndrome, we prioritize proven, stable technologies unless there's a compelling reason to adopt something new.
Main Phases of Architecture Design Process at ScienceSoft
1.
Our architecture design team collaborates with business analysts to review the documented functional and non-functional requirements and constraints. The decisions made here set the foundation for the architecture, directly impacting the system’s ability to meet user needs, performance expectations, and regulatory compliance.
2.
We determine which aspects (e.g., scalability, performance, cost-efficiency) are most critical, choose an overarching architectural style (e.g., microservices, event-driven, layered, or serverless), and establish key design principles like modularity, loose coupling, and compliance with industry standards (e.g., HIPAA, GDPR).
3.
We decompose the software into logical (e.g., front end, back end, database, external services) and functional modules, define their interactions, and plan how they'll be distributed across the physical or cloud-based infrastructure.
4.
We select the technologies and tools for each module, evaluating them based on compatibility with requirements, future scalability, licensing costs, and long-term maintenance.
5.
We create detailed architectural diagrams, including component relationships, data flows, and deployment strategies.
6.
We document the specifications for each module, outlining APIs, data formats, protocols, and inter-module communication standards. These specifications ensure seamless collaboration between teams, reduce integration risks, and provide a clear roadmap for developers.
7.
(optional) We develop proof-of-concept models to test critical aspects and address any identified risks, revising the architecture as needed.
8.
We iteratively adapt the architecture to the feedback from developers, QA engineers, and stakeholders and revisit architectural decisions when requirements evolve.
Key Architecture Design Deliverables
Architecture vision document
Includes high-level business objectives, key requirements from stakeholders, and an overview of the proposed solution.
System architecture diagram
Provides a visual representation of the system's structure with a description of all key components, their interactions, data flow, and deployment topology.
Component design specifications
Provides detailed technical designs of individual components, including their APIs, dependencies, responsibilities, and interaction details.
Technology stack recommendations
Clarifies the rationale for selecting specific technologies and tools, explores alternatives, and explains trade-offs.
Data architecture design
Covers database schemas, data flow diagrams, data models, and strategies for ensuring data security and integrity.
Integration and API strategy
Details API designs, protocols, data exchange formats, and integration points with existing systems and third-party services.
Security architecture plan
Outlines security policies, authentication and authorization mechanisms, encryption strategies, and compliance requirements.
Scalability and performance plan
Describes strategies for load balancing, caching, performance testing, and ensuring the system can scale efficiently.
Risk assessment and mitigation plan
Includes a risk matrix, risk impact analysis, and contingency plans.
Deployment strategy
Defines the deployment pipeline, environment configurations, and rollback procedures to ensure smooth launches.
Prototype or proof of concept (PoC)
Demonstrates critical aspects of the chosen architecture to test their feasibility, validate assumptions, and gather feedback.
Roles and Responsibilities in Software Architecture Planning
Software architect or technical lead
- Defines the overall architectural vision, ensuring it aligns with business requirements and technical feasibility.
- Selects architectural patterns, technologies, and tools to meet system and project requirements.
- Balances trade-offs between competing priorities (such as performance vs. cost).
- Identifies technical risks associated with architectural decisions and develops mitigation strategies.
Software engineers
- Provide practical insights into potential bottlenecks or optimization opportunities based on hands-on development experience.
- Collaborate with architects to implement proofs of concepts and prototypes, validate ideas, and refine designs.
Database engineer
- Designs database schemas to ensure efficient and accurate data storage.
- Implements strategies to maintain data integrity.
- Ensures smooth integration of the database with other components.
DevOps engineer
- Integrates CI/CD pipelines into the architecture.
- Designs scalable cloud solutions and infrastructure-as-code templates.
- Plans monitoring and logging systems.
Security engineer
- Designs and implements a comprehensive security and governance framework for the software, ensuring robust data protection and compliance with necessary standards or regulations.
- Identifies potential security vulnerabilities in the architecture and proposes safeguards, such as encryption, secure authentication mechanisms, and role-based access controls.
QA engineers
- Identify potential flaws or inefficiencies in the architectural design.
- Ensure that architecture supports software testability.
Project manager
- Ensures that the architectural decisions are aligned with the project scope, budget, and timelines.
- Identifies risks from the project delivery and management perspective and develops mitigation strategies.
* A broad pool of additional talents can join the architecture planning team on demand: compliance experts, big data engineers, data scientists, blockchain developers, and more.
Key Back-End Architecture Types We Build
General architecture types
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Monolithic architecture A monolith is a single, unified codebase where all components are tightly interconnected and interdependent. Its key advantage is simplicity in development, testing, deployment, and management. Additionally, tightly coupled components and centralized data storage in a monolith ensure maximum data consistency and lowest latency. Best for: small to medium-sized applications with simple requirements; systems requiring fast data exchange and maximized consistency, such as trading systems. |
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Service-oriented architecture (SOA) SOA organizes software into discrete, modular services that communicate using protocols like SOAP and REST or enterprise service buses (ESB). Each service is usually designed for a specific business function (e.g., PIM, CRM, ERP) and operates independently, allowing shutdowns, updates, or modifications without disrupting the rest of the system. Best for: enterprises that need to modularize their operations into reusable services or connect a variety of systems into a cohesive, scalable environment. |
Cloud-native software architectures
Cloud-native applications are designed specifically for cloud environments, leveraging cloud services and infrastructure for scalability, resilience, and flexibility. These applications often use microservices, containers (e.g., Docker), orchestration tools (e.g., Kubernetes), CI/CD pipelines for automated development and deployment, and built-in mechanisms for automated horizontal scaling and fault handling.
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As a type of service-oriented architecture (SOA), microservices refine and extend SOA principles by focusing on even smaller, granular services that operate independently. Each service focuses on a specific task (e.g., handling user access, sending emails, processing payments, or tracking orders), communicates with others using lightweight protocols like HTTP/REST or message queues, and often manages its own database. Best for: large-scale applications requiring high scalability, flexibility, fault-tolerance, and rapid deployment cycles. Also, microservices are commonly used in cloud-native applications. |
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Serverless architecture Serverless architecture breaks applications into small, stateless functions that are executed in managed compute environments. Developers can focus solely on application logic, while the cloud provider automatically handles resource allocation and scaling based on demand. Such architectures are based on function-as-a-service (FaaS) platforms like AWS Lambda, Azure Functions, and Google Cloud Functions, along with managed cloud services for databases, storage, and authentication. Best for: routine operations that require a lot of data processing capacity (like ETL, video processing, and log analysis); applications with unpredictable workloads (like ecommerce websites during sales events and IoT applications with variable input rates); applications requiring rapid and cost-efficient scaling (like generating personalized web pages or emails in real time based on user behavior). |
When to use event-driven architectures (EDA)
An event-driven architecture (EDA) is often a natural fit for microservices and serverless architectures. EDA centers on components communicating through signals called events — notifications that something important just happened. Unlike direct requests, events are sent to a central hub (like a message broker) that routes them to the appropriate components for processing. The beauty of this setup is its flexibility and speed. It allows systems to handle tasks simultaneously, making it ideal for real-time scenarios like fraud detection or live tracking — whether monitoring vehicle locations in logistics, inventory levels in warehouses, or user activity on digital platforms. Plus, because each component operates independently, changes or failures in one part don’t disrupt the whole system.
Solution-centric software designs
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Best for: product development or scaling a product for a large customer base; enabling more efficient use of resources for internal software applications that support multiple business units or users. SaaS architectures enable software delivery in the cloud and are designed to accommodate multiple users while employing cloud resources efficiently. If it’s a single-tenant SaaS, each customer has a dedicated instance of the software, offering greater customization and security but at a higher operational cost. If it’s a multi-tenant SaaS, a single instance serves multiple customers, maximizing resource utilization and scalability. |
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AI/ML and data science architectures Let’s add: An example of a predictive analytics architecture for underwriting in insurance. Best for: predictive analytics, recommendation systems, fraud detection, and computer vision applications. Artificial intelligence, machine learning, and data science architectures are tailored for extracting, processing, and analyzing data to handle complex datasets and generate actionable insights or automate decisions. They integrate components like data lakes, forecasting engines, ML model training modules, dashboards, and BI tools. Maintaining these systems is more complex due to the need for ongoing model training, updates, and tuning. |
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Event stream processing (ESP) architectures Best for: systems requiring immediate analysis and responses, like IoT applications monitoring real-time sensor data, retail inventory updates, and social media alerts. ESP architectures are meant for low-latency processing of continuously arriving data streams. They focus on performing real-time tasks such as filtering irrelevant data, aggregating event information (e.g., real-time sales totals), and transforming data for lightweight analytics or immediate decision-making. The critical elements of ESP architectures are event stream processors (e.g., Apache Flink, Kafka Streams), a data ingestion layer to integrate events from various sources, and a data sink to forward processed results to databases, dashboards, or downstream systems. |
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Big data processing architectures Best for: software solutions that collect, process, and react to high-velocity and high-variety data, e.g., ecommerce, ride-sharing, and gaming. Big data processing architectures handle massive volumes of structured, semi-structured and unstructured data. They focus on scalable and comprehensive processing and analytics that often involve AI/ML technologies to deliver deeper insights, accurate forecasting, and intelligent decision-making. The core elements of big data architectures include distributed storage and processing frameworks like Hadoop and Apache Spark. Big data architectures are usually based on either the Lambda pattern (for combining real-time and batch processing for historical analytics) or the Kappa pattern (for faster, real-time-focused data processing). For more information, explore our guide to big data processing architectures. |
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Best for: blockchain networks, cryptocurrencies, transparent voting systems, and decentralized finance (DeFi). Decentralized architectures distribute control and data across multiple autonomous nodes, eliminating a single point of failure. Nodes communicate and collaborate directly or with minimal central coordination, often using consensus protocols like Proof-of-Work or Proof-of-Stake. |
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Peer-to-peer (P2P) architectures Best for: file sharing (e.g., BitTorrent), streaming and collaboration platforms. P2P architectures create a network where each peer acts as both a client and a server, directly sharing resources and data without central servers. Such structures lower operational costs, enhance privacy, and improve scalability. |
Designing for resilience
Resilient architectures are the backbone of mission-critical applications, be it financial systems, healthcare software, telecommunications networks, and enterprise solutions that power essential business operations. The goal of resilient architecture design is to keep your business running smoothly, even in the face of unexpected failures or disruptions. We implement backup servers that are ready to take over instantly in case of downtime, database replicas to prevent data loss, and smart load balancers to redirect traffic to healthy systems. Add in self-healing mechanisms, circuit breakers, and advanced monitoring tools that detect and address issues before they impact operations, and you’ve got a system built for reliability.
Key Front-End Architectural Styles We Follow
Model-View-Controller (MVC) architecture
MVC divides an application into three components:
- Model: Manages data and business logic (back end).
- View: Handles the user interface (front end).
- Controller: Connects the Model and View, handling user inputs and updating the UI.
By separating UI and business logic, this structure improves code organization and maintainability.
Model-View-ViewModel (MVVM)
MVVM introduces a ViewModel as an intermediary between the View and Model that enables two-way data binding, keeping the UI and data in sync automatically.
This pattern is widely used for creating interactive and responsive applications.
Model-View-ViewModel-Coordinator (MVVM-C)
MVVM-C enhances MVVM by adding a Coordinator layer that manages navigation, transitions, and dependencies between Views and ViewModels. This simplifies the ViewModel, letting it focus solely on preparing data and handling user interactions. Such structure improves modularity, testability, and code clarity and is particularly popular in mobile app development.
Progressive web application (PWA) architecture
PWAs blend the best features of web and native apps. They are accessible via a browser but offer capabilities such as offline access, push notifications, and the option to save them as icons on phones or desktops. PWAs provide a fast, reliable, and cost-effective way to deliver engaging user experiences without requiring separate app installations.
Single-page application (SPA) architecture
SPAs load a single HTML page and dynamically update content as users interact with the app, eliminating full-page reloads. To enable this, SPAs rely on client-side rendering and use asynchronous communication with the server to fetch and display data dynamically.
Micro frontends
Micro frontends divide the user interface into independently developed and managed components, such as product search and filters, product catalog, and shopping cart, similar to how microservices handle back-end systems. This approach enables faster development, easier maintenance, and improved scalability for complex applications.
Software Architectures Enabling Scalability and Resilience for Our Clients
What Our Clients Say
The solution developed by ScienceSoft fully met the client's requirements and expectations. The project marked the start of successful and mutually beneficial cooperation between Leo Burnett and ScienceSoft. We appreciate their proactive approach and ability to suggest improvements to a prospective solution on both architectural and business levels. We know we can always rely on ScienceSoft’s various competencies when our clients require quality software which would facilitate their business success.
What stood out was ScienceSoft's proactive suggestions for cost-saving architecture design and tech stack solutions. Their input ensured we stayed within budget without compromising on software quality. The value we derived from partnering with ScienceSoft is definitely worth the investment. We would absolutely love to retain ScienceSoft as a long-term insurtech development partner.
In less than 4 months, ScienceSoft’s team performed a discovery phase to help us define an optimal approach to crypto wallet development. They came up with a go-to architecture, features, and tech stack for our multi-chain wallet, and introduced a detailed roadmap for crypto wallet implementation, including cost and time estimates. We decided to continue the cooperation with ScienceSoft and would certainly recommend them as a reliable blockchain tech partner.
Denis Valcu
CEO
We had a dedicated, professional team who understood my vision for the platform quite quickly and managed to translate the vision into reality which was very important to us. Not only did they understand and build, but they also advised us on the best decisions and even helped us improve some features. And what I also liked was that for each situation, they consider several scenarios to deliver the best solutions.
Abdullah Najashi
CEO
ScienceSoft showed expert understanding of MVVM-C app architecture, Swift and Node.js development skills, and knowledge of Twilio services. We would definitely recommend them to anyone looking for a competent and reliable mobile app development vendor.