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.

Getting up to speed on technical knowledge is critical for Product Managers not only because it helps you to talk with engineers, but oftentimes these components go hand in hand with Product Strategy. Take APIs for instance. Understanding APIs is important for product managers and leaders for several reasons: - APIs are the building blocks of modern software development. - They enable different systems to communicate and share data seamlessly. APIs improve product functionality by allowing customers to use data in unique ways. - They streamline development processes. APIs are also a powerful tool for understanding how customers interact with your data. By releasing API access to customers early on, you can gain valuable insights into their data consumption habits. This agile approach allows you to design a user interface that truly meets their needs after delivering value through the API. For example, at a previous company, we transformed multiple data products into a unified platform. By providing customers with data APIs first, we observed how they utilized the information. This data-driven approach enabled us to develop a platform interface that delivered immediate value and effectively addressed customer requirements. Far more than just technical necessities – APIs are strategic assets that serve as the connective tissue between software systems, enabling seamless communication and data sharing. For product managers, understanding APIs allows you to envision and implement features that leverage existing technologies, accelerating time-to-market and reducing development costs. When it comes to designing APIs, security is paramount. Poorly designed APIs can become cyber-attack gateways. Robust authentication and authorization mechanisms are non-negotiable. APIs should also be well-documented and user-friendly for smooth developer integration. So don’t think of APIs as *just* a technical component. APIs are strategic enablers driving business growth and innovation. They enhance interoperability, foster collaboration, and enable data-driven insights. Mastering API management is essential for product managers aiming to stay competitive. To help you better understand the APIs of your product, I've created an API Strategy Design Canvas with interview questions. Download it to gain deeper insights into your APIs and how internet works. Learn more about 'APIs' in our Tech Fundamentals course at https://lnkd.in/gKrpruPW 🚀 #APIs #ProductStrategy #Innovation #TechLeadership

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

𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗝𝗪𝗧 𝗧𝗼𝗸𝗲𝗻𝘀 . . JWT (JSON Web Tokens) is an open standard (RFC 7519) that defines a compact and self-contained way for securely transmitting information between parties as a JSON object. In simpler terms, JWT is a token format for sending information that can be verified and trusted because it is digitally signed. Here's how it works in a typical client-server interaction: 𝟭. 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻: The client sends its credentials (username and password) to the server for authentication. 𝟮.𝗧𝗼𝗸𝗲𝗻 𝗖𝗿𝗲𝗮𝘁𝗶𝗼𝗻: Upon successful authentication, the server creates a JWT token that contains a payload of information and a secret key. 𝟯.𝗧𝗼𝗸𝗲𝗻 𝗘𝘅𝗰𝗵𝗮𝗻𝗴𝗲: The server sends this JWT token back to the client, which stores it for future requests. 𝟰.𝗧𝗼𝗸𝗲𝗻 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: For subsequent requests, the client sends the JWT token in the request header. The server validates the token using the secret key to ensure its authenticity and integrity. 𝟱. 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: If the token is valid, the server processes the request and sends back the response. If the token is invalid or expired, the server responds with an error, and the client may need to re-authenticate. JWT tokens are widely used in modern web applications for their simplicity, flexibility, and security features. Understanding how they work can be beneficial for developers working with authentication and authorization systems. P.S. Feel free to add your thoughts on this in the comments #nodejs #backenddevelopment #jwt #security #secops

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

𝗧𝗵𝗲 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗥𝗼𝗹𝗲 𝗼𝗳 𝗔𝗣𝗜𝘀 𝗶𝗻 𝗔𝗜 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 As AI continues reshaping technology, well-designed APIs have become essential for seamless integration. APIs are the bridge between LLMs and real-world applications, determining how accessible and scalable AI functionalities become. 𝗪𝗵𝘆 𝗔𝗣𝗜𝘀 𝗠𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 APIs dictate how AI capabilities interact with existing systems. A well-structured API ensures smooth communication, prevents misuse, and enhances usability. Choosing the right API design is crucial: ✔️REST: Ideal for most web-based AI services. ✔️GraphQL: Allows clients to request only the data they need. ✔️WebSockets: Enables real-time AI applications with continuous data exchange. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: 𝗔 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗳𝗼𝗿 𝗔𝗜 𝗔𝗣𝗜𝘀 Exposing AI functionalities via APIs comes with security challenges. Best practices include: ✔️Authentication: Use OAuth 2.0 or JWT for identity verification. ✔️Authorization: Control access to AI resources. ✔️Rate limiting: Prevent excessive API calls for resource-heavy AI models. ✔️Encryption: Protect sensitive data processed by AI. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗔𝗜 𝗔𝗣𝗜 𝗗𝗲𝘀𝗶𝗴𝗻 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀 Avoid these pitfalls to ensure efficient API performance: ✔️Misusing HTTP methods (e.g., using POST for everything). ✔️Inconsistent resource naming and poor error handling. ✔️Overlooking caching strategies and proper documentation. The truth is APIs are more than just “connectors”—they shape how AI is deployed and scaled. Investing in strong API design ensures long-term success in AI-driven applications. #AI #APIs #AIIntegration #Developers #Tech

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?

I am convinced that Engineers who can connect tools, systems, and data through APIs will have a massive advantage.   Yet most engineers never learn how to work with APIs during their studies. I think that needs to change.   Engineering education still focuses heavily on deep domain expertise such as civil, maritime, mechanical, and aerospace. While essential, it no longer fully reflects how engineering is evolving in this new engineering era.   We’re entering a world where APIs, and possibly soon MCPs (Model Context Protocols), are central to how engineers design, analyze, and deliver projects.   APIs are the backbone of a digital ecosystem. But few engineers learn to think in systems, modularity, or interoperability. How do you link simulations, data, and CAD? Create intelligent workflows that span multiple platforms?   Does this mean engineers need to become developers? I don't think so. Just like we don’t write our own solvers, we learn to understand and apply them. Similarly, we don’t all need to code APIs, but we must know how to use and integrate them.   Tomorrow’s engineers will master their domain and orchestrate simple and complex workflows.   Do you agree? What skills should the next generation of engineers develop?   #DigitalEngineering #APIsInEngineering #LLMsInEngineering #SystemsThinking #Interoperability #MCP #ModelContextProtocols

🔥 Legacy assumptions collide with modern AI APIs — and the collision is a wake-up call. For over a decade, Google told developers that Google API keys “aren’t secrets” — safe to embed in HTML, JavaScript, or client-side code because they were simply billing identifiers and not sensitive credentials. That guidance made sense… until the rise of GenAI changed the game. What Truffle Security Co. uncovered is both subtle and seismic: once the Gemini (Generative Language) API is enabled on a Google Cloud project, those same public API keys quietly become authentication keys for sensitive LLM endpoints — without warnings, without prompts, and without developer awareness. Thousands of keys embedded in real-world apps (even Google’s own sites) now can: 🔓 Access private uploaded files and cached data 💸 Run up AI bills against your account 🚪 Expose internal resources to anyone who scrapes them from public pages 💡 For Developers: This flips a long-standing assumption on its head. Keys you believed were harmless identifiers are now secret credentials in disguise. That means: • Zero trust for “public” keys — treat them as sensitive by default • Audit and rotate old keys immediately if GenAI APIs are enabled • Enforce strict API key restrictions, scopes, and least-privilege design 🛡️ For Cybersecurity Pros: This isn’t just another misconfiguration — it’s a retroactive privilege escalation triggered by platform evolution, not developer error. Threat actors can extract keys from public frontends and gain access to sensitive APIs without ever touching your infrastructure. This calls for: ✔ Rapid inventory of API keys across cloud services ✔ Integration of API key scanning into risk assessments (e.g., CI/CD, repos, public assets) ✔ Education for Dev teams on evolving threat models around AI services 👉 The larger lesson? As AI platforms grow and embed into traditional cloud ecosystems, trust boundaries must be reevaluated constantly. What was once “safe to publish” can become a liability overnight — and attackers are already looking for exactly those cracks. Stay curious, stay defensive, stay ahead. #Cybersecurity #AppSec #DevSecOps #CloudSecurity #AI #GenAI #GoogleCloud #APISecurity #ThreatIntel #SecureDevelopment #RiskManagement #BugBounty #CTI #DevCommunity #SecureCoding #ZeroTrust