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When you build backends for dozens of startups, patterns emerge—not just in code, but in what actually works at scale, under pressure, and with small teams.
At TLVTech, we’ve refined a set of backend design patterns we use across most projects—regardless of stack, industry, or company size. These patterns help us deliver faster, onboard developers quickly, and avoid messy rewrites later on.
Here’s what we use, and why it works.
We don’t start with microservices. We start with a modular monolith.
Why:
Once there’s real scale or organizational need, we extract services with clear boundaries. Premature microservices = wasted time and complexity.
We keep backend layers clean and predictable:
Why:
This structure works across NestJS, Express, Django, Spring—doesn’t matter. Clean separation always wins.
We use Data Transfer Objects (DTOs) to define inputs and outputs between layers.
Why:
Especially useful in TypeScript and Python with tools like class-validator or Pydantic.
We use async event patterns (pub/sub or message queues) selectively—mainly for:
Why:
We prefer lightweight solutions like Redis streams or AWS SNS/SQS before going full Kafka.
Every project has:
Why:
Observability starts with consistent logging.
We don’t rely on magic. We centralize config using .env files, secrets managers, or config services—so nothing is hardcoded.
Why:
Backend systems don’t win because they’re clever. They win because they’re predictable, understandable, and built to grow.
The design patterns we use are boring on purpose—because boring is what makes products stable, scalable, and easy to maintain.
If you’re building something and want backend that won’t crumble under growth, let’s talk.
- Android app development begins with Android Studio - a tool specifically tailored to help in code writing and debugging. - Create a project using templates to lay down foundational building blocks for your Android app. - Project files (activities, layouts, manifest files) serve different app structure purposes. - Modify the app user interface through Android Studio's Layout Editor and Attributes Panel for changes such as background color or padding. - Designing interface involves XML to structure your app’s view, and the combination of graphics and colors for final UI/UX. - Advanced topics include building apps for multiple screens, camera integration, and cloud services integration in apps. - Essential tools for Android app development include Android Studio, Android SDK for functions like GPS, data storage, and Firebase for aspects like data storage and user authentication. - Certifications in Android app development include Associate Android Developer Certification (tests ability to code android apps in Java or Kotlin), Google Play Store Listing Certificate (grants proficiency in managing your app in the Play Store), and Android Kotlin Developer Nanodegree (certifies proficiency in Kotlin for Android application development).
- Google Vision API is a machine learning tool capable of analyzing images, and can identify objects, texts, faces, and landmarks. - The API can be integrated by creating a project on Google Cloud Console, enabling the API for the project, and making REST API calls. - Key functionalities include optical character recognition with translation capability, object and face detection, image analysis, and detection of explicit content. - To get started, install Google Vision API using Python and 'pip install', then setup for image recognition by: creating a Google Cloud Project, enabling Vision API, downloading a private key, and pointing the `GOOGLE_APPLICATION_CREDENTIALS` variable to that key. - Google Vision API operates with a tiered pricing structure; it isn't free, and cost increases with use. - AutoML, integrated in Google Vision API, simplifies model training by automating the process. It works both online and offline, categorizes images, and detects objects. - To code with Google Vision API in Python, libraries have to be imported, followed by creating an instance for image analysis, and then calling the API operations.
- Microservices are small, independent apps forming a full application; each can be built, deployed, and scaled separately. - Kubernetes, an open-source container orchestration tool, deploys, scales, and monitors microservices; enhancing flexibility and control. - Containers in microservices are standalone software units bundling code and its dependencies, ensuring applications run quickly and reliably. - Docker helps with microservices deployment by encompassing the application and its requirements into one package, thus promoting portability. - Spring Boot supports microservices by creating standalone applications that require minimal setup and dovetail well with microservice architecture. - Kubernetes, with its node and master structure, deploys and manages your applications over several instances and efficiently handles scaling. - Quarkus, a Java platform ideal for microservices, works in conjunction with Kubernetes for faster startup and low memory use. - OpenShift, a PaaS tool, aids in deploying microservices by teaming up with Kubernetes to create an automated environment.
