This isn't a textbook architecture framework — it's the reference Taliferro actually uses, assembled from real system design work at Accenture, T-Mobile, and AT&T, where a wrong call meant an outage at scale, not a bad grade. Organized into groups so you can jump to what's relevant to the decision in front of you, instead of scrolling through 25 flat headings to find it.
Co-Founder Taliferro
This started as personal notes from system design work at Accenture, T-Mobile, and AT&T, where a wrong architectural call meant an outage at scale, not a bad grade — and it's been kept current since with cloud-native patterns, CI/CD practices, and lessons from newer enterprise environments. It's organized into six groups: foundations, application and state, network and security, data and cloud services, the app layer, and delivery. Each entry covers roughly the same three things: a summary of what it is, how the architectural issue it addresses actually gets handled, and guidance for choosing between alternatives where they exist. See also Controlling Cloud Cost and Building Ethical AI.
A diagram should indicate the critical capabilities of an Application Architecture. These capabilities determine what applications are necessary to deliver on the business vision. This consists of a summary of the types and numbers of assets, data requirements, asset characteristics, and so on, for each Application.
A middleware layer describes the various mechanisms by which consumers access data.
The Data layer can be described separately as part of the Data Architecture or as part of the application architecture.
The discussion of the Data Architecture should include a description of the enterprise data access methods, direct and replicated local data access methods, and guidelines regarding data location.
An integration plan provides the data access method used.
A project's technical architecture can comprise multiple environments — usually integration, staging, and production. Staging isn't an active environment; it's designed to mirror production and assume its activities for real-world testing.
The cloud provides redundancy and failover through configuration rather than custom code. A well-architected system should meet the challenges of:
Typically, best practice development in the SPA world aligns with an aggregate approach instead of a series of rows. The consequence of querying a row/series in a SPA application has a negative effect because the query only needs a subset of data from some entities, such as a summary of services for several microservices without all of the service details. If the SPA must extract all of the data for the relevant entities to obtain the required information, it will negatively impact the user experience. A best practice for SPA applications is to generate, in advance, a view that materializes the data in a format suited to the required component.
The view should only contain the data required by a particular SPA component. Materialized views can include values from calculations or other data items. You can even optimize the materialized view for just a single query. If the source changes, the view can be updated to include the new information automatically via a cloud function/database trigger.
Context management refers to storing and retrieving information entered by the user across a sequence of screen exchanges or page submissions. Specifically, provide facilities that enable an application programmer to :
Consider a context framework that gives developers access to context through a single interface. Examples of context components are:
Establish a virtual network to handle middleware execution and resource retrieval. All the resources communicate outbound to the internet and inbound with cloud components.
Within the virtual network, there can be a dedicated data subnet. The advantage to breaking the data and app environment up into a subnet is that it allows the division of one network into logical subnetworks, separating data duties from middleware and web resource duties.
An app environment subnet is responsible for all business logic, web service, and resource access.
If utilizing Active Directory, a component can serve as an identity management service and filter applications' access by enforcing security rules. In addition, the AD service can facilitate Single Sign On (SSO).
NSGs filter network traffic to and from resources in a virtual network.
The purpose of the security approach is to provide a consistent infrastructure for ensuring that only authorized users can access web services and that those users can only access the data pertinent to their function.
A Data Factory will issue custom commands to extract data from external sources.
A Data Lake can work in conjunction with a storage blob for data that is structured, imported through a batch process, and used for streaming analytics and machine learning.
DSs can be used as a repository for big data analytics workflow, either unstructured, semi-structured, or structured data. Automatically replicates for unexpected hardware failures and has unlimited size and no limits to scale.
Works as a cloud-to-cloud implementation where cloud-based VMs and data can be backed-up to the Backup Vault without disrupting services.
Virtual machines can act as gateways to extract data from external data stores.
It can provide the service necessary to harness all the data collected and apply data science to the acquired data.
If there is data extraction from many systems, the data can grow exponentially. The data is possibly unstructured, or the information is in a binary format. A blob stores the data and can be used for analysis by other services.
Data scientists use the machine learning workspace to conduct experiments on the data.
Use an SQL database to create custom data tables, enforce referential integrity, and run necessary stored procedures to generate the data for UI needs.
The only difference between the WebApp and an API App is that the WebApp does not contain a defined interface. The WebApp is usually the Node.js portion of a project.
The middleware (with interfaces) contains the business logic that returns the data.
Code is stamped and frozen, and this code version is pushed to an integration environment where all code is tested as a single unit. Once bugs are fixed, each team pushes the revised code to the source control repository. The code is stamped with a minor version change and moved to the integration server.
If there are no bugs, the release manager reviews the results with the product owner, then pushes the code to a staging server.
A QA team performs quality acceptance testing on the code, testing for bugs, performance, and user story match. The QA team sends a bug report (if applicable) back to the development team for bug fixes. Each development team reviews the QA report and fixes bugs found in staging.
This video explains how Taliferro approaches design with accountability, fairness, and scalability — principles at the core of modern system architecture.
Logical architecture maps capabilities and relationships; physical architecture maps those capabilities to concrete services, networks, and storage. Keep them separate to clarify decisions.
At minimum: integration, staging, and production. For regulated workloads, add a validation environment mirroring production for change control.
Automated tests on every commit, artifact versioning, environment-specific configs, one-click deploys, and rollback. Add change-freeze windows only when justified.
When components repeatedly need aggregated data across entities. Precompute views to cut latency and reduce over-fetching.
Tie decisions to measurable outcomes: latency, resiliency targets, risk reduction, and cost-to-serve. Use a one-page ADR summary with options, trade-offs, and a clear recommendation.
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