Understanding API Development

Whether you're a 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿, 𝗱𝗮𝘁𝗮 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹, 𝗔𝗜 𝗽𝗿𝗮𝗰𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗿, or 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿, a solid understanding of how APIs work—and how to design them well—is non-negotiable in today’s tech landscape. To make this easier, I’ve created an 𝗶𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 that breaks down the 𝗰𝗼𝗿𝗲 𝗽𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗼𝗳 𝗔𝗣𝗜 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁—from architecture to scalability. Here are the 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 you’ll find valuable: → 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗔𝗣𝗜𝘀: Public, Private, Composite—each has a specific use case depending on access control, modularity, and system integration patterns. → 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗮𝗹 𝗖𝗵𝗼𝗶𝗰𝗲𝘀: Understand REST for stateless simplicity, GraphQL for flexible querying, and Webhooks for real-time event-driven communication. → 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝘆 𝗗𝗲𝘀𝗶𝗴𝗻: Implement OAuth 2.0, JWT tokens, and TLS encryption—not just to protect data, but to meet compliance and scale securely. → 𝗧𝗼𝗼𝗹𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗘𝗻𝗮𝗯𝗹𝗲𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝘃𝗶𝘁𝘆: Use 𝗦𝘄𝗮𝗴𝗴𝗲𝗿/𝗢𝗽𝗲𝗻𝗔𝗣𝗜 for consistent documentation, 𝗣𝗼𝘀𝘁𝗺𝗮𝗻 for thorough testing, and 𝗔𝗣𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆𝘀 for versioning and rate-limiting. → 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗥𝗮𝗽𝗶𝗱 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁: Choose based on team strengths and application context—𝗙𝗮𝘀𝘁𝗔𝗣𝗜, 𝗦𝗽𝗿𝗶𝗻𝗴 𝗕𝗼𝗼𝘁, 𝗘𝘅𝗽𝗿𝗲𝘀𝘀.𝗷𝘀, or 𝗙𝗹𝗮𝘀𝗸 can all help streamline backend workflows. → 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗟𝗼𝗻𝗴𝗲𝘃𝗶𝘁𝘆: Apply RESTful conventions, ensure consistent error handling, support API versioning, and document with clarity. → 𝗗𝗼𝗻’𝘁 𝗼𝘃𝗲𝗿𝗹𝗼𝗼𝗸 𝘁𝗵𝗲 𝗹𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: API development doesn’t end at deployment. Monitoring, logging, deprecation planning, and backward compatibility are just as important. 𝗔𝗣𝗜𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝘁𝗼𝗼𝗹𝘀—𝘁𝗵𝗲𝘆’𝗿𝗲 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗶𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲𝘀. And how you build them determines the reliability, usability, and future extensibility of your software. Whether you’re building your 𝗳𝗶𝗿𝘀𝘁 𝗔𝗣𝗜 or managing 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗺𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻, these principles will guide you toward scalable, secure, and developer-friendly design. 𝗪𝗵𝗮𝘁’𝘀 𝗼𝗻𝗲 𝗹𝗲𝘀𝘀𝗼𝗻 𝗼𝗿 𝘁𝗼𝗼𝗹 𝘁𝗵𝗮𝘁’𝘀 𝗵𝗲𝗹𝗽𝗲𝗱 𝘆𝗼𝘂 𝗺𝗼𝘀𝘁 𝗶𝗻 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗣𝗜𝘀? Let’s share and learn—drop your thoughts in the comments.

In 2002, Jeff Bezos mandated Amazon to be an API first company. In 2025, our thesis at NextBillion.ai is that API first companies will benefit the most from the agentic revolution we are seeing. API's will connect to other API's in a secure, fast and reliable manner doing human tasks that we today do using clicks or scrolls. Moreover, developer role in integration will reduce dramatically giving a whole new set of audience to access and work with software like never before. How will this work? 1) Fast Agents will need massive usage of API's to execute complex tasks in a short span of time. Ex in our world: Routing 10k parcels with 50 business constraints. Today you log in, upload your file/csv, take a CRM dump, connect many systems and execute this task. Then you distribute this via the same clunky UI based software to 100 drivers. All this will be ONE single API call. AI systems will use APIs to execute hundreds of thousands of complex processes in a short time. 2) Secure AI agents will access data through APIs. All the UI based software companies built legacies promising security and data handling at a robust level. But with the new flow, API first companies that have robust security systems will benefit the most. Ex: Order data, customer data and location data is critical in logistics and companies that have been handling this via secure API's will just plug into LLM agents to do the same tasks saving millions of $ in both headcount costs and software licenses 3) Documentation The beauty of #API based software is the no human bottle neck to implement. With agents able to understand complex tasks well already, it's a matter of time that they can read documentation and implement software on their own. As with everything, Bezos saw the future way before most of us. Ex: They built S3 as an internal tool before making it a full scale foundational product in AWS. Similarly even at Google, a lot of the products they built were initially for internal use that become massive revenue drivers. The AI Revolution is powered by data, and data is served by APIs. #software #agents #llm

How to Improve API Performance? If you’ve built APIs, you’ve probably faced issues like slow response times, high database load, or network inefficiencies. These problems can frustrate users and make your system unreliable. But the good news? There are proven techniques to make your APIs faster and more efficient. Let’s go through them: 1. Pagination ✅ - Instead of returning massive datasets in one go, break the response into pages. - Reduces response time and memory usage - Helps when dealing with large datasets - Keeps requests manageable for both server and client 2. Async Logging ✅ - Logging is important, but doing it synchronously can slow down your API. - Use asynchronous logging to avoid blocking the main process - Send logs to a buffer and flush periodically - Improves throughput and reduces latency 3. Caching ✅ - Why query the database for the same data repeatedly? - Store frequently accessed data in cache (e.g., Redis, Memcached) - If the data is available in cache → return instantly - If not → query the DB, update the cache, and return the result 4. Payload Compression ✅ - Large response sizes lead to slower APIs. - Compress data before sending it over the network (e.g., Gzip, Brotli) - Smaller payload = faster download & upload - Helps in bandwidth-constrained environments 5. Connection Pooling ✅ - Opening and closing database connections is costly. - Instead of creating a new connection for every request, reuse existing ones - Reduces latency and database load - Most ORMs & DB libraries support connection pooling If your API is slow, it’s likely because of one or more of these inefficiencies. Start by profiling performance and identifying bottlenecks Implement one optimization at a time, measure impact A fast API means happier users & better scalability. ✅

Thinking of API design? You are essentially defining a strict conversation protocol between two entities. 𝗗𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴 𝗥𝗘𝗦𝗧𝗳𝘂𝗹 𝗔𝗣𝗜𝘀 requires more than just knowing endpoints; it demands a mastery of the semantic intent behind HTTP Methods and the precise communication of Status Codes. Here is the breakdown of the flow: 𝟭. 𝗧𝗵𝗲 𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗖𝘆𝗰𝗹𝗲 (𝗧𝗵𝗲 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻) The client initiates a connection. In a real-world scenario, this involves DNS resolution, a TCP handshake, and TLS negotiation before the first byte of HTTP is even sent. The request carries the "Method" (the verb) and the "URI" (the noun). 𝟮. 𝗛𝗧𝗧𝗣 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 (𝗧𝗵𝗲 𝗔𝗰𝘁𝗶𝗼𝗻𝘀) Choosing the right verb is critical for "Idempotency" and "Safety": • 𝗚𝗘𝗧 (See it): A safe, idempotent operation. It retrieves data without side effects. As shown in the sketch: "Can I see the toy?" • 𝗣𝗢𝗦𝗧 (New one): Non-idempotent. It instructs the origin server to create a subordinate resource. "I made a new drawing." • 𝗣𝗨𝗧 (Change it): Idempotent. Replaces the target resource with the request payload. If you retry a PUT request N times, the state on the server remains the same as if you did it once. • 𝗗𝗘𝗟𝗘𝗧𝗘 (Throw away): Removes the resource. 𝟯. 𝗦𝘁𝗮𝘁𝘂𝘀 𝗖𝗼𝗱𝗲𝘀 (𝗧𝗵𝗲 𝗖𝗼𝗻𝘁𝗿𝗮𝗰𝘁) The server's response isn't just data; it's a status report. The sketch categorizes these beautifully by color logic: • 𝟮𝘅𝘅 (𝗚𝗿𝗲𝗲𝗻/𝗦𝘂𝗰𝗰𝗲𝘀𝘀): The handshake worked. 200 OK is standard, but 201 Created is specific to POST/PUT operations resulting in new resources. • 𝟯𝘅𝘅 (𝗬𝗲𝗹𝗹𝗼𝘄/𝗥𝗲𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻): Crucial for SEO and migration. 301 tells a search engine the move is permanent; 302 implies it's temporary. • 𝟰𝘅𝘅 (𝗥𝗲𝗱/𝗖𝗹𝗶𝗲𝗻𝘁 𝗘𝗿𝗿𝗼𝗿): The "Oops, You!" category. 400 Bad Request means malformed syntax, while 404 Not Found means the URI maps to nothing. This saves server processing power by rejecting early. • 𝟱𝘅𝘅 (𝗢𝗿𝗮𝗻𝗴𝗲/𝗦𝗲𝗿𝘃𝗲𝗿 𝗘𝗿𝗿𝗼𝗿): The "My Bad" category. 500 is a generic failure, often an uncaught exception. 503 Service Unavailable often signals a gateway timeout or maintenance mode. Mastering these codes means you can debug systems faster. Instead of guessing why an API failed, the code tells you exactly who is at fault: the sender (4xx) or the receiver (5xx). Save this cheat sheet. It is the grammar of the web. #http #WebDevelopment #SystemDesign #APIs #SoftwareEngineering

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

10 Must-know best practices for optimizing API endpoints: Optimizing API endpoints is critical for achieving optimal performance in robust, scalable, and user-friendly applications. By following best practices, we can significantly enhance performance, strengthen security, and improve user and developer experience of APIs. Let's look at 10 core best practices for optimizing API endpoints: 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 🔹 Optimize SQL queries Ensure your queries are performant. Use query execution plans to identify bottlenecks. Optimize and implement caching for frequent queries to minimize database load. 🔹 Cache Store frequently requested data at the client, server, or CDN level using caching headers or tools like Redis. This reduces response time and lightens backend load. Be mindful of stale data and implement cache invalidation strategies. 🔹 Payload optimization Compress large responses with Gzip, remove unnecessary fields from payloads, and use efficient formats like JSON for faster data transmission. Keep payloads lightweight, but don’t compromise on essential details for the client. 🔹 Pagination Break large datasets into smaller chunks with tools like limit and offset parameters. This improves performance and avoids crashing clients with oversized responses. Combine with cursors for better consistency in real-time data. 🔹 Asynchronous processing For time-intensive operations like file uploads or report generation, use background jobs with tools like RabbitMQ or Celery to keep APIs responsive. Return task IDs so clients can check the operation's status. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗯𝗲𝘀𝘁 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 🔹 Rate limiting and throttling Set limits on requests per user or client to prevent abuse, avoid server overload, and ensure consistent performance during traffic spikes. Customize thresholds based on endpoint sensitivity. 🔹 Input validation and sanitization Validate and sanitize all user inputs to protect against injection attacks (e.g., SQL injection, XSS) and ensure data integrity. 🔹 Monitoring and logging Track API metrics like response times, error rates, and usage patterns using tools like Datadog or New Relic. Comprehensive logs simplify debugging and help predict scaling needs. Regularly review logs to identify trends or anomalies. This is also important to identify performance bottlenecks. 🔹 Authentication and authorization Implement robust mechanisms like OAuth2, API keys, or JWT to ensure secure access and restrict resource usage to authorized users. 🔹 Encrypting data in transit Use HTTPS to secure data exchanges between clients and servers, ensuring sensitive information remains protected from interception. 💬 What’s your favorite API optimization tip? 💭 ~~ P.S. If you like this post, then you'll love our newsletter. Subscribe here: https://lnkd.in/giQj3Z44

Elevate Your Software Development with REST APIs In the dynamic world of technology, Application Programming Interfaces (APIs) are the linchpin, enabling seamless communication between applications. REST APIs, known for their simplicity and scalability, stand out as key facilitators of innovation. Understanding APIs and REST: APIs empower developers to integrate systems, enhance functionalities, and foster innovation. REST APIs, with their flexibility and stateless nature, have gained widespread popularity in facilitating seamless data exchange. Key Principles for REST API Design: REST Methods: Utilize standard HTTP methods (GET, POST, PUT, DELETE) for specific purposes. Cacheable: Optimize performance by allowing responses to be cached. Stateless: Each client request contains all necessary information, ensuring no server-side storage of client state. Simple & Fine-Grained: Design should offer fine-grained control over resources for efficient manipulation. Pagination, Filtering, Ordering: Support for handling large datasets and flexible data retrieval. Connectedness: Resources should be interconnected, promoting discoverability. Security: Implement secure authentication and authorization mechanisms for data protection. Best Practices for Implementation: Versioning: Support versioning to manage changes without breaking existing client implementations. Self-Descriptive Messages: Responses include metadata for better understanding and utilization. HATEOAS: Promote API discoverability by allowing clients to navigate through resource representations. Layered System & Uniform Interface: Enhance modularity and user experience through a consistent and layered design. CORS & Idempotence: Enable controlled cross-origin resource sharing and ensure predictability in operations. Authentication, Logging, Input Validations: Prioritize security, reliability, and data integrity. TLS & Rate Limiting: Encrypt communication and implement rate limiting for performance and security. Conclusion: Designing a robust REST API is an art that shapes the interconnected landscape of the digital world. These principles serve as a comprehensive guide for architects and developers, unlocking the true potential of REST APIs. As technology advances, well-designed APIs will continue to drive innovation and evolution in modern applications.

API Design Mistakes to Avoid 🔥 Good APIs save time. Bad ones create technical debt and frustrate developers. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 Inconsistent Naming • 𝗠𝗶𝘅𝗲𝗱 𝘀𝘁𝘆𝗹𝗲𝘀: /create_user, /getUserDetail • Standardize: Use RESTful patterns like /users. No Versioning • 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 affect existing clients. • Add versions: GET /api/v2/users. Over-fetching/Under-fetching • 𝗧𝗼𝗼 𝗺𝘂𝗰𝗵 𝗼𝗿 𝘁𝗼𝗼 𝗹𝗶𝘁𝘁𝗹𝗲 𝗱𝗮𝘁𝗮 impacts performance. • Use query parameters: GET /users/{id}?fields=name,email. Poor Error Handling • 𝗚𝗲𝗻𝗲𝗿𝗶𝗰 𝗲𝗿𝗿𝗼𝗿𝘀: "Something went wrong!" • Be explicit: 404: User not found.The user with the provided ID does not exist. ✅ Best Practices • Consistent Naming: 𝗔𝗱𝗼𝗽𝘁 𝗼𝗻𝗲 𝗰𝗼𝗻𝘃𝗲𝗻𝘁𝗶𝗼𝗻 and follow it. • Versioning: Start with clear 𝗔𝗣𝗜 𝘃𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴. • Controlled Fetching: 𝗔𝗹𝗹𝗼𝘄 𝗱𝗮𝘁𝗮 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 via parameters. • Clear Errors: 𝗥𝗲𝘁𝘂𝗿𝗻 𝗺𝗲𝗮𝗻𝗶𝗻𝗴𝗳𝘂𝗹 𝘀𝘁𝗮𝘁𝘂𝘀 𝗰𝗼𝗱𝗲𝘀 and messages. APIs are long-term assets. Build them with clarity, consistency, and scalability in mind. 𝗪𝗵𝗶𝗰𝗵 𝗔𝗣𝗜 𝗺𝗶𝘀𝘁𝗮𝗸𝗲 𝗱𝗼 𝘆𝗼𝘂 𝘀𝗲𝗲 𝗿𝗲𝗽𝗲𝗮𝘁𝗲𝗱 𝗼𝘃𝗲𝗿 𝗮𝗻𝗱 𝗼𝘃𝗲𝗿 𝗮𝗴𝗮𝗶𝗻? ________________________ 📷 I'm Nina, Software Tech Lead & PM, crafting tech visuals engineers love. I called them Sketech, easy to find for tech minds. Sketech has a LinkedIn Page. Join me! ❤️

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?