Understanding API Development

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?

A REST API can be easy to build. And painful to use. The problems are often not hidden deep in the code. They show up in the design choices you make early: → Inconsistent endpoint names → No pagination until the data grows → Error messages that explain nothing → Breaking changes for small updates → Security added after the API is already live Each one makes life harder for the developers using your API. A good API should feel predictable. You should be able to guess how endpoints are named. You should get useful errors when a request fails. You should be able to fetch only the data you need. And adding a new field should not break every client. Five rules I try to follow: 1. Keep resource names simple and consistent 2. Design for change before creating a new API version 3. Add pagination and filtering from the start 4. Return errors that help the client fix the request 5. Treat auth, permissions, and rate limits as core design work Your API is not only a way to expose data. It is something other developers have to trust. Before you ship your next API, check whether you are making any of these five mistakes: https://lnkd.in/dCc9kHXV

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

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?

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

🚨 𝗔𝗣𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗟𝗼𝗼𝗸𝘀 𝗘𝗮𝘀𝘆 Until your API is used by dozens of applications, hundreds of developers, and millions of requests every day. Over the years, I've noticed that most API issues don't come from code. They come from design decisions made early in the lifecycle. A few principles consistently make APIs easier to scale, maintain, and evolve: 1️⃣ Resource Naming Matters Use clear, predictable resource names. ✅ /users ✅ /orders ✅ /payments The goal is for consumers to understand the API without reading extensive documentation. 2️⃣ Consistency Beats Creativity Use plural resources consistently. Follow predictable URL patterns. Avoid mixing conventions across services. Consistency reduces onboarding time and integration errors. 3️⃣ Design For Relationships Resources rarely exist in isolation. Examples: 🔹 Users and Orders 🔹 Customers and Payments 🔹 Posts and Comments A well-designed resource hierarchy makes APIs easier to navigate and understand. 4️⃣ Idempotency Is Essential Retries happen. Network failures happen. Duplicate requests happen. An API should handle these scenarios safely without creating inconsistent data. This becomes especially important for payments, orders, and transaction processing systems. 5️⃣ Security Must Be Built In Authentication and authorization should never be an afterthought. Secure APIs typically include: 🔹 OAuth 2.0 / OIDC 🔹 JWT Validation 🔹 Rate Limiting 🔹 Request Validation 🔹 Audit Logging 6️⃣ Versioning Protects Consumers APIs evolve. Clients often don't. Versioning provides a controlled path for introducing change without breaking existing integrations. 7️⃣ Pagination Is Not Optional Large datasets eventually become performance problems. Pagination improves: ✅ Response times ✅ Database efficiency ✅ User experience ✅ Infrastructure costs What I'd Add Beyond The Diagram Modern production APIs also need: 🔹 Observability 🔹 Distributed Tracing 🔹 Rate Limiting 🔹 Circuit Breakers 🔹 Structured Error Responses 🔹 API Contracts & Documentation 🔹 Backward Compatibility Strategies The best APIs aren't necessarily the most feature-rich. They're the ones developers can understand, trust, and integrate with quickly. What API design principle has saved you the most pain in production? #APIDesign #Microservices #Java #SpringBoot #SoftwareArchitecture #SystemDesign #DistributedSystems #AWS #Kubernetes #OAuth2 #GraphQL #Kafka #BackendEngineering #CloudComputing #Observability #PlatformEngineering #TechLeadership #C2C#EngineeringLeadership #SoftwareEngineering #C2H #EnterpriseArchitecture

Most engineering teams think APIs are integration layers. The best engineering organisations treat APIs as operational infrastructure. That distinction matters far more in 2026 than most teams realise. As AI systems, distributed services, event-driven workflows, and platform ecosystems expand… APIs are becoming the control plane of modern software architecture. Which means poor API design no longer creates only technical inconvenience. It creates operational friction, scalability limitations, security exposure, and long-term architectural debt. 𝐓𝐡𝐞 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐬𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐚𝐫𝐞 𝐧𝐨𝐰 𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐬𝐢𝐧𝐠: → Reliability over feature velocity → Governance over uncontrolled integration growth → Observability over reactive debugging → Standardisation over fragmented interfaces → Resilience over short-term optimisation 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐦𝐨𝐝𝐞𝐫𝐧 𝐀𝐏𝐈𝐬 𝐚𝐫𝐞 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐬𝐞𝐫𝐯𝐢𝐧𝐠 𝐨𝐧𝐥𝐲 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬. 𝐓𝐡𝐞𝐲 𝐚𝐫𝐞 𝐬𝐞𝐫𝐯𝐢𝐧𝐠: → AI agents → Autonomous workflows → Multi-cloud platforms → Enterprise ecosystems → Real-time operational systems And that changes the design expectations entirely. The organisations building durable engineering advantage are not necessarily shipping the most APIs. They are building APIs that remain scalable, observable, secure, and adaptable under continuous change. Because in modern software systems… Architecture quality compounds. P.S. Many teams still evaluate APIs based on functionality. The more mature engineering organisations evaluate them based on long-term operational behaviour. Follow Umair Ahmad for more insights

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