Guide to Different Types of Architecture for Research Data
Use our chart and glossary to understand the different types of architecture for your research data and build the right one for your institution or project.
To make the most of your research data, it’s important to understand the different types of architectures and build the right one for your institution or project. The following chart and glossary will give you an overview of what different types of architecture are responsible for.
What it covers
- Data sources
- Data flows
- Data technologies (extract, transform and load, or ETL)
- Data analytics
- Data visualisation
Data architecture describes the data sources, develops data workflows, defines the data extractions technologies (e.g. ETL), analyses data models and implements data analytics, data science decisions and visualisation.
What it covers
- Software/hardware
- API
- Services (Kubernetes)
- Design conceptual models (structure)
Systems architecture defines a conceptual model that describes the structure and behaviour of multiple components and subsystems such as software modules, databases and network devices. It provides a conceptual strategy for the implementation of complex systems, defining how the different components of the system interact and work together to achieve the desired functionality and performance. Systems architecture aims to ensure that the system meets the intended requirements, such as reliability, scalability, security and maintainability.
What it covers
- Infrastructure and technical systems
- Physical and virtual components (servers, networks, storage, software)
- Implementing new technologies
Infrastructure architecture (IA) deals with developing and managing an organisation’s technical infrastructure, which includes servers, storage, network systems, and virtualization technologies. It deals with the technical elements of the IT environment. IA is the foundation upon which everything else is built.
What it covers
- Conceptual structure and operation of organisation
- Unified Architecture Framework (UAF)
- Standard using UML
- Digital transformation
Enterprise architecture looks (EA) at the organisation as a whole, including its people, processes, information, and technology. It ensures that the organisation’s business strategy and objectives are aligned with its technology and operations. This has become a priority for organisations attempting to keep up with new technologies such as the cloud, IoT, machine learning and other developing trends that will drive digital transformation. Enterprise Architects closely work with business leaders and IT teams. EA helps organisations manage complex information by implementing maintenance models using unified modelling language (UML), systems modelling language (SysML) and other open standards.
What it covers
- Unified security design
- Documentation on potential risks
- Security standardisation
- Vulnerability management
Security architecture defines security principles, models and techniques that work together to keep organisations secure from cyber-attacks. Security architects document potential risks and provide vulnerability management solutions.
What it covers
- AI architecture and pipeline
- Data management / big data
- ML model deployment
- Data-driven decision-making
- Accuracy scoring
ML/AL architecture creates, builds, implements and operates an end-to-end machine learning (ML) and AI pipeline. AI architects collaborate with data architects, scientists and data engineers to help establish a solid enterprise-wide data and AI architecture.
What it covers
- Storage and computation architecture
- Cloud platform, storage, server
- Visualisation
- Cloud computing service models (PaaS, SaaS, IaaS)
Cloud architecture is responsible for an organisation’s cloud computing strategy. This includes cloud application design, adoption plans, management and monitoring. Cloud architect works around platform-as-a-service (PaaS), software-as-a-service (SaaS) and infrastructure-as-a-service (IaaS).
What it covers
- Software development
- Maintenance of software systems
- Collaboration with software developers
- Technology stack and code review
Software architecture provides a blueprint for developing, deploying and maintaining software applications. A software architect can troubleshoot code issues and collaborate with other software experts to create high-performance software systems. Effective software architecture can lead to improved development efficiency, reduced risks and a higher-quality end product.
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