Key Factors to Consider in API Development

My first API caused outages. My tenth didn’t. The 10 API principles that survive contact with production: 1. Ship business truth, not database columns Design your contracts around real domain actions and entities. Internal schemas evolve. Your API is the promise you can’t break. 2. Consistency beats cleverness Pick one naming style, one error format, one approach to pagination, one authentication strategy. Your consumers shouldn’t need a decoder ring. 3. Don’t expose implementation details Hide the storage model, hide job orchestration, hide temporary hacks. Clients should never notice your system changes. 4. Errors must teach, not confuse Include a clear message, machine-readable code, and actionable guidance. A great error cuts support tickets in half. 5. Version on breaking change only Expect change. Plan for it. V1, V2, sunset plans, and adapters. Consumers should upgrade because they want improvements, not because you broke them. 6. Rate limits are product decisions Define limits based on behavior you want. Reward good usage patterns. Protect yourself from abuse. Make thresholds visible and predictable. 7. Idempotency everywhere Clients retry. Networks glitch. Duplicate requests happen. Use idempotency keys on write operations so your business rules stay correct. 8. Validate at the edges Everything that crosses the boundary gets validated: shape, type, length, enums, security. Trust nothing at runtime except what you check. 9. Performance is part of the contract Fast responses turn your API into a dependency people love. Measure latency. Optimize the hot paths. 10. Observability isn’t optional Trace every call. Log context. Surface meaningful metrics. When something fails, you must see the “why” within minutes. Key takeaways • Treat APIs as long-term promises • Make behavior obvious, errors useful, and change safe • Control misuse with clear rules, not hidden traps • Build the level of visibility you’ll want at 3am when things break What did I miss?

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

APIs aren't just endpoints for data engineers - they're the lifelines of your entire data ecosystem. Choosing the Right API Architecture Can Make or Break Your Data Pipeline. As data engineers, we often obsess over storage formats, orchestration tools, and query performance—but overlook one critical piece: API architecture. APIs are the arteries of modern data systems. From real-time streaming to batch processing - every data flow depends on how well your APIs handle the load, latency, and reliability demands. 🔧 Here are 6 API styles and where they shine in data engineering: 𝗦𝗢𝗔𝗣 – Rigid but reliable. Still used in legacy financial and healthcare systems where strict contracts matter. 𝗥𝗘𝗦𝗧 – Clean and resource-oriented. Great for exposing data services and integrating with modern web apps. 𝗚𝗿𝗮𝗽𝗵𝗤𝗟 – Precise data fetching. Ideal for analytics dashboards or mobile apps where over-fetching is costly. 𝗴𝗥𝗣𝗖 – Blazing fast and compact. Perfect for internal microservices and real-time data processing. 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁 – Bi-directional. A must for streaming data, live metrics, or collaborative tools. 𝗪𝗲𝗯𝗵𝗼𝗼𝗸 – Event-driven. Lightweight and powerful for triggering ETL jobs or syncing systems asynchronously. 💡 The right API architecture = faster pipelines, lower latency, and happier downstream consumers. As a data engineer, your API decisions don’t just affect developers—they shape the entire data ecosystem. 🎯 Real Data Engineering Scenarios to explore: Scenario 1: 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 Challenge: Process 100K+ transactions/second with <10ms latency Solution: gRPC for model serving + WebSocket for alerts Impact: 95% faster than REST-based approach Scenario 2: 𝗠𝘂𝗹𝘁𝗶-𝘁𝗲𝗻𝗮𝗻𝘁 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 Challenge: Different customers need different data subsets Solution: GraphQL with smart caching and query optimization Impact: 70% reduction in database load, 3x faster dashboard loads Scenario 3: 𝗟𝗲𝗴𝗮𝗰𝘆 𝗘𝗥𝗣 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 Challenge: Extract financial data from 20-year-old SAP system Solution: SOAP with robust error handling and transaction management Impact: 99.9% data consistency vs. 85% with custom REST wrapper Image Credits: Hasnain Ahmed Shaikh Which API style powers your pipelines today? #data #engineering #bigdata #API #datamining

Gathering API Requirements as a Business Analyst APIs are like the invisible bridges between systems. But here’s the catch: if a BA doesn’t ask the right questions, those bridges often collapse midway. Over the years, I’ve seen BAs struggle with API requirements because they sound too “technical.” The reality is—it’s about asking structured, practical questions. Here’s how a BA should approach it: 1. Understand the Business Flow First Example: In a loan origination system, when a user submits an application, the system needs to fetch the applicant’s credit score from a 3rd-party provider. 👉 First question: “At what stage do we call the API to pull the credit score?” 2. Define Inputs & Outputs Clearly Example: For the credit score API: Input = Applicant’s SSN & DOB Output = Credit Score, Credit History Status 👉 Ask: “What information do we send? What should we receive back?” 3. Discuss Error Scenarios Example: What if the credit bureau service is down? 👉 Requirement: “System should show a friendly error and allow retry after 5 minutes.” 4. Talk About Security & Access Example: APIs often need authentication (API Keys, OAuth). 👉 Question to ask: “Who generates the token? How often does it expire?” 5. Map Data Fields to Business Terms Example: API returns credit_status = 01. 👉 Requirement: “Map 01 to ‘Excellent’ on the user interface.” 6. Define Performance & Volume Needs Example: If 10,000 applications come in during peak season, can the API handle that load? 👉 Capture: “API should support 100 requests per second.” Don’t just think of APIs as “technical stuff.” Treat them as part of the business workflow. Your role is to ensure that when two systems “shake hands,” they do it smoothly, securely, and with the right data. BA Helpline

Stop measuring your API product like it’s a standard SaaS app. NPS is lying to you. A developer might give you a 9/10 on a survey because your support team is nice, while simultaneously struggling for 4 hours to authenticate a request. If you are a Product Manager in an API-first company, "Daily Active Users" isn't enough. You need to measure friction. Here are the 4 DX metrics that actually matter (and how to track them): 𝟭. 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗙𝗶𝗿𝘀𝘁 "𝗛𝗲𝗹𝗹𝗼 𝗪𝗼𝗿𝗹𝗱" (𝗧𝗧𝗙𝗛𝗪) This is your North Star. The Metric: The timestamp difference between a user signing up and their first successful API call. < 15 mins: World class. 1 hour: Acceptable. > 1 day: Your churn is happening here. The Fix: If this is high, your onboarding flow is broken, or your "Getting Started" guide is burying the lede. 𝟮. 𝗧𝗶𝗺𝗲 𝘁𝗼 𝗙𝗶𝗿𝘀𝘁 𝗞𝗲𝘆 (𝗧𝗧𝗞) Before they make a call, they need credentials. The Metric: How many clicks/seconds does it take to generate a production (or sandbox) API key? I’ve seen platforms hide API keys behind three settings menus and a mandatory "Contact Sales" form. Don't do this. Self-serve access is the baseline expectation. 𝟯. 𝗧𝗵𝗲 "𝗦𝘁𝘂𝗺𝗯𝗹𝗲" 𝗥𝗮𝘁𝗲 (𝟰𝘅𝘅 𝘃𝘀. 𝟮𝘅𝘅) Look at your logs. The Metric: The ratio of client-side errors (400s) to successes (200s) during the first week of integration. If 60% of calls to your /checkout endpoint return a 400 Bad Request, your documentation for that endpoint is unclear. The user isn’t stupid; your parameter definitions are. 𝟰. 𝗦𝗗𝗞 𝘃𝘀. 𝗥𝗮𝘄 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗨𝘀𝗮𝗴𝗲 The Metric: What percentage of traffic comes from your official SDKs (Python, Node, Go) vs. raw curl/http requests? High SDK usage: Your libraries are providing value and saving time. Low SDK usage: Your libraries might be bloated, outdated, or poorly documented (use Archbee to share the docs and keep them updated — automagically), forcing devs to roll their own wrappers. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀! In B2B API sales, the developer is the technical buyer. If you can prove to your VP of Sales that you reduced Integration Time from 3 weeks to 3 days, you didn't just improve DX. You shortened the sales cycle. Stop optimizing for "delight." Optimize for velocity. 𝗧𝗵𝗲 𝘂𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻: Go ask your engineering lead today: "Do we actually know our average Time-to-First-Hello-World?" If the answer is "No," you know what to put on next quarter's roadmap. 👇 How do you currently measure success for your developer portal? Drop your top KPI below.

Most teams start by building features. The best ones begin by laying solid foundations. One of the most underrated foundations I’ve seen is an API-first mindset. It’s not just a technical choice, it’s more of a cultural one. It says, “We believe clarity, collaboration, and scalability matter just as much as shipping speed.” When you design your APIs before the UI or logic, you are treating the API as the product’s backbone, not a side effect. It becomes the shared language between engineering, design, and product, a living contract that keeps everyone aligned. Front-end teams can mock and test early. Back-end teams can focus on business logic. Integrations don’t have to wait. And suddenly, handoffs stop feeling like hurdles. From a scaling perspective, this approach quietly future-proofs your product. A well-designed API makes it effortless to plug in new features, connect partners, or evolve your system without pulling everything apart, which is exactly how Stripe, Twilio, and Shopify built thriving ecosystems around their platforms. So how do you actually do API-first design? ✅ Start with the contract. Define endpoints, data models, and expected behaviors before writing a single line of code. Tools like Stoplight, SwaggerHub, and Postman make this process seamless. This becomes the source of truth. ✅ Adopt the OpenAPI Specification. OpenAPI (formerly Swagger) helps you define your API in a standard, machine-readable format. This ensures documentation and mock servers stay in sync automatically. ✅ Prioritize consistency and naming conventions. Keep your endpoints predictable, plural for resources, and verbs for actions. Good API design is as much about clarity as capability. ✅ Version and test early. Decide on a versioning strategy upfront (e.g., /v1/users) and build tests that validate your contract. Once APIs are public, breaking changes get expensive. ✅ Document like your reputation depends on it. Because it does. Great APIs live or die on their documentation. Use Postman Collections or ReadMe.io to keep it friendly and up to date. Want to go deeper? Here are some excellent resources: API-first development with Postman: https://lnkd.in/gWskYv_b Google API's design: https://lnkd.in/g6ysvEcQ

Mastering REST API Design: Key Principles, Methods & Best Practices if you're building modern applications, mastering REST API Design is non-negotiable. A well-designed API improves performance, scalability, developer experience, and long-term maintainability. Here’s a structured breakdown of the fundamentals highlighted in the visual 👇 🔶 Core REST Principles A robust REST API is built on: 🧩 Client–Server Architecture 🌀 Stateless Interactions 🔗 Uniform Interface 🏛️ Layered System ⚡ Cacheable Responses 🧩 Optional: Code-on-Demand These principles ensure consistency, performance, and scalability. 🔷 Design Essentials To make your API intuitive and developer-friendly: 📘 Simple & fine-grained endpoints 🔤 Clear resource naming 🔍 Filtering & ordering 📑 Pagination (first, last, next, prev) 🔄 Versioning for backward compatibility 🛠️ Monitoring & caching for performance 🔐 Security & Reliability First A production-ready API must include: 🔑 Authentication & authorization 🌐 CORS handling 🛡️ TLS security ✔️ Input validation 📉 Rate limiting 📝 Logging ♻️ Idempotency in critical operations 🧰 REST Methods You Should Master GET → Retrieve data POST → Create a resource PUT → Update/replace DELETE → Remove a resource Keeping methods predictable improves usability and reduces confusion. 💡 Takeaway: A great REST API isn’t just functional — it’s reliable, secure, scalable, and enjoyable for developers to work with. 💬 What’s one REST design best practice you never compromise on? Share your thoughts below!

𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 𝗥𝘂𝗹𝗲𝘀 𝗘𝘃𝗲𝗿𝘆 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗞𝗻𝗼𝘄 Great systems are built on great APIs. APIs are the contracts between services, clients, and teams. A badly designed API leads to confusion, bugs, rework, and eventually, broken systems. Here are 8 API design rules I wish I learned earlier: 🔹 Consistency is king: Clear, predictable naming builds trust. /users/{id}/orders should behave the same today and tomorrow. 🔹 Version your APIs: Systems evolve. Prefixing with /api/v1/... ensures you don’t break clients when features change. 🔹 Proper methods & idempotency: GET for reads, POST for create. Make retries safe, your future self will thank you. 🔹 Handle errors gracefully: Return meaningful status codes and error bodies. A good API explains what went wrong. 🔹 Think of the client: Design for ease. Pagination, filtering, and solid docs make integration painless. 🔹 Use standard conventions: Stick to RESTful principles. Avoid clever hacks that confuse teams and tools. 🔹 Secure your endpoints: Use proper auth (like OAuth2) and restrict access. Don’t expose more than necessary. 🔹 Document like a pro: APIs should be self-explanatory. Swagger/OpenAPI docs speed up onboarding and reduce guesswork. What’s one API design rule you wish more developers followed? Learn more about APIs: 𝟖 𝐑𝐄𝐒𝐓 𝐀𝐏𝐈 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰: https://lnkd.in/d5axY7xr #softwareengineering #apidesign #systemdesign #interviewtips #apigateway #webdevelopment #backenddeveloper

If I were just starting out with APIs, these are the 10 rules I’d follow. These best practices will help you create simple, clear, and consistent APIs that are easy to use and understand. 1/ Keep It Simple   ↳ Use clear, concise endpoints that describe resources.   ↳ Avoid over-complicating; keep naming consistent and understandable.   ↳ Example: `/books` for all books, `/books/{id}` for a specific book. 2/ Use RESTful Design   ↳ Use standard HTTP methods: GET, POST, PUT, DELETE.   ↳ Name endpoints with nouns like `/users` or `/orders` for clarity.   ↳ Example: HTTP code 200 (success), 404 (not found), 500 (server error). 3/ Choose Standard Data Formats   ↳ Use JSON as it’s readable and widely supported.   ↳ Keep data formats consistent across endpoints.   ↳ Example: `{ "title": "To Kill a Mockingbird", "author": "Harper Lee" }`. 4/ Provide Clear Documentation   ↳ Document endpoints with detailed descriptions.   ↳ Provide request and response examples for easy usage.   ↳ Example: Explain `/users/{id}` with request/response samples. 5/ Implement Versioning   ↳ Include versioning in the URL to manage changes.   ↳ Allow for updates without breaking existing clients.   ↳ Example: `/v1/books` for version 1, `/v2/books` for an updated version. 6/ Ensure Security   ↳ Use HTTPS for data encryption.   ↳ Implement authentication and authorization mechanisms.   ↳ Example: OAuth 2.0 to secure user access to APIs. 7/ Handle Errors Gracefully   ↳ Use standard HTTP status codes like 400, 404, and 500.   ↳ Provide informative error messages to help resolve issues.   ↳ Example: `400 Bad Request` for invalid input, with a detailed error message. 8/ Optimize Performance   ↳ Use caching to store frequent responses and speed up access.   ↳ Apply rate limiting to control the number of requests a user can make.   ↳ Example: Cache popular books, limit requests to prevent server overload. 9/ Test Thoroughly   ↳ Conduct functionality, performance, and security testing.   ↳ Ensure different user scenarios are tested for reliability.   ↳ Example: Use automated tools for end-to-end testing before deployment. 10/ Monitor and Update   ↳ Monitor API performance and user activity continuously.   ↳ Update the API to address bugs or add features regularly.   ↳ Example: Use Prometheus to monitor latency and health. – P.S: What would you add from your experience?