Techlusion’s Post

Hey 👋🏽 all! 🚀 Exciting Tech Insights Alert! 🚀 Just dropped a new article that explores the intricate connection techniques in the MERN Stack - from Front-End to Back-End. 🌐✨ In this four-part series, we journey through RESTful API Integration, the flexibility of GraphQL, the real-time magic of WebSockets, and the simplicity of Fetch API in Vanilla JS. Each technique brings its unique advantages to bridge the gap between the front-end and back-end of modern web applications. 🚀 📖 Read the full article below ⬇ ⬇ 🔍 Highlights: • Understanding RESTful API Integration and its principles. • Establishing a RESTful API in Express.js. • Consuming RESTful services in React using Fetch API and Axios. 🌐 Table of Contents: 1. RESTful API Integration 2. GraphQL for Flexible Data Fetching 3. Real-Time Updates with WebSocket 4. Fetch API in Vanilla JS 💡 Engage with me: What's your go-to connection technique in the MERN Stack? Let's discuss in the comments! 💬 https://lnkd.in/gpdm3kGK #TechTalk #MERNStack #WebDevelopment #FrontEnd #BackEnd #TechArticle

GraphQL API in a nutshell: 1. What is GraphQL? GraphQL was developed by Facebook in 2012 and later open-sourced in 2015. It's a query language for your API and a runtime for executing those queries by using a type system you define for your data. 2. Key aspects of GraphQL include: - Declarative Data Fetching: Clients can request exactly the data they need, and nothing more. This eliminates over-fetching or under-fetching of data, improving efficiency. - Single Endpoint: Unlike REST, which may require multiple endpoints for different resources, GraphQL uses a single endpoint for all queries and mutations. -Strongly Typed: GraphQL APIs have a strong type system that's defined using a schema. This schema serves as a contract between the client and server, ensuring consistency. - Real-time Data: GraphQL can be used for real-time applications by utilizing subscriptions. Subscriptions allow clients to receive updates when the underlying data changes. - GraphQL Schema At the core of a GraphQL API is the schema, which defines the types of data that can be queried and the operations that can be performed. A GraphQL schema consists of: - Types: These represent the data structures in your API, like User, Product, or Post. - Queries: Entry points for fetching data. Clients use queries to request specific data from the server. - Mutations: Entry points for modifying data. Clients use mutations to create, update, or delete data. - Subscriptions: These are used for real-time updates. Clients can subscribe to changes in the data. 3. GraphQL Servers To implement a GraphQL API, you need a server that understands GraphQL queries, resolves them, and returns the requested data. Popular options for building GraphQL servers include: - Apollo Server: A widely-used GraphQL server library for Node.js. - Express-GraphQL: Middleware for adding GraphQL to Express.js applications. - Graphene-Django: A Python library for building GraphQL APIs with Django. - GraphQL Clients Clients use GraphQL to request data from the server. 4. Some popular GraphQL client libraries include: - Apollo Client: A JavaScript library for making GraphQL queries and mutations in web and mobile applications. - Relay: A JavaScript framework for building efficient and scalable GraphQL-powered applications, mainly by Facebook. 5. Use Cases for GraphQL GraphQL is well-suited for a variety of use cases: - Mobile Applications: GraphQL's flexibility allows mobile apps to request only the data they need, saving bandwidth and improving performance. - Real-Time Applications: With subscriptions, GraphQL is suitable for chat apps, live feeds, and other real-time systems. - Aggregating Data: GraphQL can serve as a single point of access to aggregate data from multiple sources. - Complex Queries: For applications requiring complex - Microservices: GraphQL can act as a facade for multiple microservices, providing a unified API

The Evolution from XML to JSON in Data Exchange Greetings, LinkedIn community! 👋 Today, let's take a journey through the serene landscape of data interchange formats, exploring the quiet transition from XML to JSON. Join me as we unravel the story behind these formats, their emergence, and the unique needs they serve. Having always been in the JSON camp throughout my career, a recent project required me to delve into the world of XML for a side-by-side comparison. The juxtaposition of these formats was intriguing. In the early 2000s, JSON, or JavaScript Object Notation, gracefully entered the scene. Born from a quest for simplicity, JSON offered a lightweight and human-readable syntax, designed to streamline data exchange. Its aim was clear: foster collaboration and understanding among developers. On the flip side, XML, or Extensible Markup Language, made its debut in the late '90s. Initially crafted for document structuring, XML found versatility as a markup language, suited for various data representation needs with its tag-based structure. As the web evolved, JavaScript became the language of choice for dynamic web development. JSON, with its native integration into JavaScript, became the perfect companion. This harmonious pairing brought tranquility to web developers, simplifying data manipulation and contributing to the rise of JSON as a preferred format. In my exploration, the serene nature of JSON stood out. Its simplicity and readability were a breath of fresh air, especially when compared to XML's more verbose markup language. The ease with which JSON aligned with JavaScript felt like a natural dance, streamlining my development process. Recent years witnessed major players like Google and Twitter quietly making the shift from XML to JSON in their APIs. This transition wasn't marked by aggression but rather a calm acknowledgment of JSON's efficiency. Google reported reduced latency, while Twitter experienced improved API response times, illustrating a gentle revolution in data exchange. In the era of microservices and RESTful APIs, JSON quietly became the darling of modern architectures. Its adaptability and ease of use led to a serene integration into distributed systems. The peaceful transition of 9 out of 10 modern APIs from XML to JSON reflects the format's embrace of contemporary tech needs. In Conclusion: JSON – A Tranquil Tech Choice! My personal journey from JSON enthusiast to a more nuanced understanding of XML has been enlightening. JSON's story is one of tranquility, simplicity, and adaptation. It's not just a format; it's a calming influence in the dynamic world of data exchange. So, if you haven't explored JSON yet, consider the peaceful journey it offers – it's serene, it's harmonious, and it's backed by a tranquil history.

Excited to share my insights and experiences with the LinkedIn community on my GraphQL journey! 🚀 As a backend developer at Root and Leaves Company, I've been diving deep into GraphQL, PostgreSQL, and Prisma. These technologies have revolutionized modern API development, and I can't wait to explore their fascinating world with you. So, what exactly is GraphQL? It's not just a buzzword; it's a game-changer in web development. Unlike traditional RESTful APIs, GraphQL gives developers the flexibility they need by allowing them to request exactly what they need, no more and no less. This control over data retrieval makes applications more efficient and responsive. The key difference between GraphQL and RESTful APIs lies in how data is fetched. With REST, you often have to make multiple requests to different endpoints, resulting in over-fetching or under-fetching of data. GraphQL, on the other hand, allows you to specify the shape of your response in a single query. Say goodbye to unnecessary requests and welcome a personalized data concierge experience. Join me on this GraphQL adventure and let's unlock the potential of modern APIs together! 🌐🔐 #GraphQL #APIs #WebDevelopment #Programming #BackendDevelopment #Database #DataQueries #RESTvsGraphQL #Prisma #PostgreSQL #DeveloperLife #CodingJourney #TechInsights #ModernAPIs #LearningAndSharing

Day 29/100: Exploring Prisma ORM for Node.js - Brief summary of what I learned: Today, I delved into Prisma, a modern ORM for Node.js and TypeScript that streamlines database interactions. Prisma provides a powerful and intuitive interface for working with databases, making it easier to manage data models and perform complex queries. - Key takeaways:  - What is Prisma?:   - Prisma is a next-generation ORM that allows you to define your database schema using the Prisma Schema Language (PSL).   - It generates a type-safe database client for Node.js and TypeScript, enhancing the developer experience by providing autocomplete, type checking, and intuitive APIs.  - Core Features:   - Prisma Schema: A declarative data modeling language to define your database schema, including models and relations.   - Prisma Client: An auto-generated and type-safe query builder for Node.js and TypeScript, making it easy to perform database operations.   - Prisma Migrate: A powerful migration tool that helps you manage schema changes over time, ensuring your database evolves alongside your application.   - Prisma Studio: A web-based GUI to interact with your database, allowing you to view and edit data easily.  - Benefits of Using Prisma:   - Type Safety: Ensures your queries are type-safe, reducing runtime errors and enhancing code quality.   - Developer Productivity: Simplifies complex queries and database interactions, boosting productivity with features like autocomplete and intuitive APIs.   - Scalability: Supports multiple databases including PostgreSQL, MySQL, SQLite, SQL Server, and MongoDB, making it suitable for a variety of use cases.   - Community and Ecosystem: Prisma has a vibrant community and extensive documentation, making it easy to get started and find support when needed.  - Use Cases:   - Building data-intensive applications with complex relational data models.   - Rapidly prototyping applications with dynamic and evolving schema requirements.   - Ensuring data integrity and consistency in large-scale production environments.   - Simplifying the transition between development, staging, and production databases with robust migration tools. - Challenges faced and how I overcame them: Learning the Prisma Schema Language (PSL) and setting up Prisma Migrate were initially challenging. I overcame these hurdles by studying Prisma’s comprehensive documentation, engaging in community forums, and experimenting with sample projects to gain practical experience. Prisma has proven to be a powerful tool in my development toolkit, and I’m excited to continue using it to build robust and scalable applications! #NodeJS #Prisma #ORM #100DaysOfCode #WebDevelopment #CodingJourney #Learning #Database #TypeScript #JavaScript #TechBlog #SoftwareEngineering

🚀 Prisma and TanStack Query come together to revolutionize full-stack development! 💥 Introducing ZenStack, a full-stack toolkit that extends Prisma's capabilities, providing modeling, access control enforcement, and automatic CRUD API generation. With ZenStack, you can achieve a Prisma-like data query experience on the frontend using TanStack Query hooks. 🧪 Generate hooks directly from your schema and leverage their power to fetch, cache, and bind data effortlessly. Plus, ZenStack's automatic query invalidation and optimistic updates simplify UI state management. 🔄 The best part? Your schema becomes your API, thanks to ZenStack's server adapters for popular frameworks like Next.js, Nuxt, Express, and more. 🌐 Join the ZenStack community on Discord and show your support on GitHub! ⭐️ #ZenStack #Prisma #TanStackQuery #frontenddevelopment #fullstackdevelopment #APIs #NodeJS #Nextjs #Nuxt #Express #TypeScript

#50daysofcodechallenge 🚀 Day 42 of my web development journey: a duo adventure into MongoDB validation and the art of visualizing data with charts! 📊💡 **MongoDB Validation with Mongoose:** Ensuring data integrity is a breeze with Mongoose's schema-based validation. Fields can be set as required, with specific formats, and more. Validation errors act as guardians, maintaining the quality of stored information. **Data Visualization with Charts:** Data transforms into insights when visualized. Today, I dived into creating charts using Chart.js, turning raw data into vibrant visuals. It's incredible how a few lines of code can make data come alive! These compact yet powerful tools elevate the quality and presentation of data. Join me for more insights. #webdevelopment #webdev #backend #frontend #developer #100daysofcode #mongodb

🔍 When it comes to data fetching in React applications ⚛️, the options are vast and varied! Let's explore some remarkable libraries and strategies 🚀: 🔸 SWR: SWR (Stale-While-Revalidate) introduces a strategy where data is initially retrieved from the cache (stale), followed by a fetch request (revalidate), ensuring the most up-to-date data. SWR continuously streams data updates to components, maintaining a fast and responsive user interface. It's the easiest library; developers import and employ the useSWR hook with a custom fetch function. 🔸 TanStack-Query: TanStack Query (FKA React Query) Often regarded as the "missing data-fetching library for web applications," react-query simplifies the process of fetching, caching, synchronizing, and updating server state in web applications. It seamlessly functions out of the box and boasts extensive functionalities, including support for window focus refetching, pagination, prefetching, scroll restoration, and more. Its ease of use, along with rich features, makes it a popular choice among developers. 🔸 RTK Query: React Toolkit Query (RTK Query) emerges as a robust tool for data fetching and caching, aiming to simplify common scenarios involved in loading data within web applications. By eliminating the need to manually craft data fetching and caching logic, RTK Query streamlines the process. Offering advanced features like optimistic updates, pagination, polling, and deduplication, it's adept at handling complex data-fetching scenarios. 🔸 Relay: Developed by Facebook, Relay (also known as "Relay Modern" in its second version) stands as an advanced framework tailored for building data-driven applications using React and GraphQL. However, embracing Relay demands a considerable amount of boilerplate code and initial setup. Even after setup, navigating through Relay might pose several questions and challenges, especially for those with limited exposure to its workings. 🔸 Apollo Client: Another robust contender in the JavaScript landscape, Apollo Client is hailed as "a comprehensive state management library for JavaScript." It adeptly handles both local and remote data using GraphQL. While akin to Relay in some aspects, Apollo Client offers a smoother setup experience. Configuring it involves defining a client and making it accessible via a dedicated provider, paving a comparatively easier path to kickstart your project. In addition to these libraries, the conventional Fetch API/Axios combination utilized with the useEffect hook remains a standard method 😉 for data fetching in React applications. 💡 Which of these libraries or techniques do you find most suitable for data fetching in your React applications? Let me know in the comments 💬For more intriguing content, follow Pruthvi Tirmal :) #datafetching #frontenddevelopment #graphql #reacthooks #reactlibrary #reactjs #reactdeveloper

In the ever-evolving landscape of technology, the ability to effectively handle and visualize large datasets is crucial. With the advent of Big Data, developers are constantly exploring innovative solutions to manage and analyze voluminous data. In this realm, the trio of React, Node.js, and MongoDB has emerged as a powerful stack for developing scalable and dynamic applications. In this article, we will delve into how these technologies can be harnessed for Big Data processing and visualization, and explore some open-source alternatives.

Want to take your development skills to the next level? Let's talk about GraphQL. GraphQL is a query language used for interaction between the server and the client side. Its unique query language allows clients to request exactly the data they need, nothing more and nothing less, For example if we have fields like profile picture, name, email, id, address and etc etc. On the front end we can tell the server that we only want the fields of id and name for the user and server will then only send those two fields in response, resulting in efficient and streamlined communication between the frontend and backend. How does it work? Unlike traditional RESTapi you don't have to create server routes. Only resolvers, Mutations and Queries. Think of them like creating functions that take input, executes the resolver, the resolver returns data ( maybe process middle wares, utility/helper functions ) that is then taken back to the client side. But what are they? Resolvers: They are the handlers, that are responsible for processing of data for example CRUD or Integration pipelines with the database. Queries: Think of them as GET method like there's in traditional RESTapi. They are solely responsible for fetching data from a source. For example database. Mutations: For all the other methods namely POST, PUT, PATCH, DELETE there is a single method for all of them, i.e. GraphQL Mutation. But how does it know what operation to perform? From the resolver where we will have already described basically a function that is invoked and does as we have described maybe like validating a token, updating the data in the database or maybe deleting it and so on ). First you define your GraphQL schema for your data in the server. Next, you import your schemas and create resolvers, run a command in the terminal that automatically generates the rest of the code for you and adds it to your frontend. Then in your frontend you'll have a configuration file for GraphQL where you can configure you directories, hooks ( generated automatically by GraphQL for you to leverage in your frontend. Amazing! isn't it? ) typescript etc etc. Once you have it configured it's time to create schemas. ( sounds tiring and difficult but it isn't, trust me ) These schemas tell the server about the fields you want in response from the GraphQL server, and in the last step you run the GraphQL command that then generates the hooks, types and Apis for you to be able to use them in your front end. Next? what else? 😅 Everything's done. What?? you still want to do some manual work? Just call the hooks, paas in the data and viola!! You have your data. Not only does GraphQL sends data, it has built in functionality to automatically send other fields such as error messages, status code and pending state. Here are some other features of GraphQL that you're gonna love. 1- Automatic typescript generation for type safety. 2- Caching 3- Automatic error and promise handling. 4- Lesser work than RestAPI