At Tiddle, we choose technology stacks that give us the flexibility to deliver secure, scalable, and reliable systems for our clients.
Managing Director, Dayton Outar, has extensive experience working with a wide range of programming tools and frameworks used to deliver enterprise-grade software systems.
By enterprise systems, we mean software platforms made up of several applications working together. These systems typically include:
- User experience applications (UX) such as web or mobile apps
- Back-end services that process information and enforce business rules
- Databases that store and protect critical information
- Integration layers that move information between multiple systems
In many cases, a middle layer connects the secure database to the user applications. This layer is responsible for validating requests, processing data, and ensuring that the right information reaches the right users.
While many frameworks can expose information over the internet, the real challenge begins when systems must handle heavy processing workloads.
Examples of heavy operations include:
- Reading thousands or millions of records from large files
- Processing financial transactions
- Handling concurrent users
- Managing long-running background jobs
- Streaming real-time events to users
Processing a small file with a few hundred records is very different from processing large files containing millions of records. At that scale, the framework must support advanced capabilities such as background workers, concurrency control, and fault tolerance.
After evaluating several technologies, we identified three frameworks that are serious contenders for enterprise-grade systems.
From our research, two stand out clearly for the type of capabilities required by financial institutions:
- Java (Spring Boot)
- .NET
Even institutions that start small must carefully consider operational risk when selecting their technology stack. Choosing a framework that later hits tooling or scalability limitations can create serious long-term costs.
For this reason, these two frameworks form the core of our back-end engineering strategy at Tiddle.
A worthy mention is Golang. Go has excellent runtime performance and concurrency capabilities. However, based on our evaluation matrix, Go currently falls short in one critical area: built-in support for targeted real-time messaging to specific users or groups.
Modern systems increasingly rely on event-driven architecture, where back-end systems broadcast updates to specific users, services, or workflows in real time. For financial platforms, where events such as payments, approvals, or alerts must reach the correct user immediately, this capability is essential.
For this reason, while Go remains an impressive technology, ASP.NET Core and Spring Boot currently provide the most complete enterprise application platforms for our needs.
| Category | Go (Golang) | ASP.NET Core | Spring Boot |
|---|---|---|---|
| Speed to prototype | 🟡 Good | 🟡 Good | 🟡 Good |
| Production scalability | 🟢 Excellent | 🟢 Excellent | 🟢 Excellent |
| Concurrency model | 🟢 Goroutines + scheduler | 🟢 Async + multithreading | 🟢 JVM multithreading |
| Worker cancellation (native) | 🟢 Yes (context.Context) | 🟢 Yes (CancellationToken) | 🟢 Yes (Future.cancel) |
| Background execution model | Goroutines / worker pools | Hosted Services / worker services | Executors / Batch / Quartz |
| Messaging ecosystem | 🟡 Kafka, NATS, RabbitMQ | 🟢 Kafka, RabbitMQ, Azure Service Bus | 🟢 Kafka, RabbitMQ, JMS |
| Real-time user targeting | 🔴 Manual (WebSockets) | 🟢 SignalR (users & groups) | 🟢 WebSocket + STOMP |
| Strong typing | 🟡 Structural | 🟢 Nominal | 🟢 Nominal |
| Compile-time safety | 🟢 Yes | 🟢 Yes | 🟢 Yes |
| Observability & tracing | 🟢 Excellent | 🟢 Excellent | 🟢 Excellent |
| Enterprise auth (LDAP, AD, SSO) | 🟡 External libraries | 🟢 Native integration | 🟢 Native integration |
| Microservice ergonomics | 🟢 Excellent | 🟢 Strong | 🟢 Strong |
| Business-domain modeling | 🟡 Verbose | 🟢 Strong | 🟢 Very strong |
| Long-term maintainability | 🟢 Strong | 🟢 Strong | 🟢 Strong |
| Typical enterprise usage | Infrastructure, platforms, SRE | Banking, SaaS, Government | Banking, Telco, Big Tech |
Speed to Prototype
This measures how quickly developers can build an initial working version of an application. A framework with good prototyping speed allows engineers to test ideas and deliver early versions of software quickly.
Production Scalability
Scalability refers to how well a system performs as usage grows. Enterprise systems must support thousands or even millions of users without slowing down or crashing.
Concurrency Model
Concurrency describes how a system performs many tasks at the same time. Modern applications often process multiple requests simultaneously, such as users logging in, uploading files, or processing transactions.
Worker Cancellation
Long-running tasks sometimes need to be stopped safely. Worker cancellation allows developers to stop operations like data processing or background jobs without corrupting data.
Background Execution Model
Many enterprise tasks run in the background, separate from the user interface. Examples include processing payments, importing large datasets, or generating reports.
Messaging Ecosystem
Enterprise systems often communicate through messaging systems such as Kafka or RabbitMQ. These systems allow services to exchange events reliably and build scalable event-driven architectures.
Real-time User Targeting
Some applications must push updates instantly to specific users or groups. Examples include notifications, financial alerts, or real-time dashboards.
Strong Typing
Strong typing helps developers catch mistakes early by enforcing strict rules about the type of data used in a program.
Compile-time Safety
Compile-time safety means many errors are detected before the program even runs, improving reliability and reducing production bugs.
Observability and Tracing
Enterprise systems need tools that help engineers understand what the system is doing internally. Observability includes logging, metrics, and tracing tools that help diagnose problems.
Enterprise Authentication
Large organizations often use centralized identity systems such as Active Directory, LDAP, or Single Sign-On (SSO). Frameworks that integrate easily with these systems simplify enterprise security.
Microservice Ergonomics
Modern systems are often built as microservices, where many small services communicate with each other instead of one large monolithic application.
Business Domain Modeling
Enterprise applications must represent complex real-world concepts such as financial transactions, accounts, contracts, and workflows. Good domain modeling tools make these systems easier to design and maintain.
Long-term Maintainability
Enterprise software may run for 10–20 years or more. Maintainability measures how easy it is for future engineers to understand, modify, and extend the system.