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Cutting Through API Complexity: A Guide to GraphQL

Have you ever felt frustrated fetching data from an API and ending up with a bunch of information you don’t need? Enter GraphQL, a game-changer in the world of server-side APIs! This blog post takes you on a journey to understand GraphQL, from its core concepts to practical development steps.

What is GraphQL?

Imagine an API that caters to your specific needs. With GraphQL, that’s exactly what you get! It’s a querying language specifically designed for server-side applications. There will be a single API endpoint. Users can request the required details and the system will send a response as per the requests with a precise answer.

The Building Blocks of GraphQL:

Everything in GraphQL revolves around three key concepts: Schema, Queries, and Mutations. 

Schema: Think of the schema as the blueprint for your data. It defines the main structure of the data that can be queried or modified, ensuring consistency and clarity for developers. The schema structure is created using fields and types.

Queries: These are used to fetch the data from the GraphQL API. You define the specific data you need in a structured format, and GraphQL gets it for you efficiently. This is a similar use case like a “GET” request in RESTful APIs.

Mutations: Need to insert, update, or delete data? Mutations are your answer. They are used to modify (Insert, Update, Delete) the data in the GraphQL API. This is a similar use case like “POST, PUT, PATCH, or DELETE” requests in RESTful APIs.

Understanding the GraphQL API Architecture:

Architecture plays a crucial role in achieving efficiency. But what exactly goes on behind the scenes? Let’s break it down!

The above diagram represents the 3-tier architecture diagram of GraphQL. Here’s what’s happening: 

  • Clients send the requests with only the required parameters in the query string using JSON format 
  • GraphQL server handles the requests with appropriate actions and interaction with the data layer, 
  • After that send back the responses with only requested data as payload JSON to the Clients for further process.

The Role of GraphiQL IDE: 

GraphiQL is a graphical interface specifically designed for GraphQL. It allows you to build, test, and debug your queries and mutations in a user-friendly environment. 

Benefits of GraphQL:

  • Precision is key: Getting exactly what we need is the primary goal for GraphQL. It delivers only the data your application needs for faster performance.
  • One request, many answers: Get multiple distinct details in a single request instead of sending multiple requests.  
  • Structure for clarity: Structured type referencing request and response detail, making it easier for developers to understand and maintain the API.
  • Future-proof flexibility:  Updates to the API become a breeze with GraphQL. You can introduce new features without depending on version control management, ensuring smooth operation. 
  • No More Data Juggling: Easily combine multiple data sources in a single endpoint.

REST vs GraphQL:

REST vs GraphQL:

In a traditional REST API, you might need to use multiple endpoints for different requests. 

With GraphQL, a single request with a well-defined structure retrieves all the data in one go, saving time and resources.

How to get started with GraphQL?

If you are considering the technology and framework to build a web application using GraphQL, you can choose Express.js.

Development Flow Diagram in Express:
Development Flow Diagram in Express:

The above diagram shows the flows that give you an overall idea to develop a project using GraphQL. If you are a beginner, please check the following link

Server-Side Application:

  • Include two npm packages (graphql-http and graphql) to start with GraphQL.
  • Build the folder structure to start the development to define the GraphQL schema.
  • Create the base folders in the following manner “/src/schema”.
  • The concept is to build module-wise schema, so it’s better to create individual folders for each module (i.e. User, Product, Order, Payment, etc.).
  • Each module folder should have 3 files (index.js, typedefs.js, and resolvers.js).
  • Use the ‘index.js’ to combine the integrations of ‘typedefs.js’ and ‘resolvers.js’ to make it centralized for that particular module.
  • Use ‘typedefs.js’ to define the structure of queries and mutations for that particular module.
  • Use ‘resolvers.js’ to build the logic to manipulate queries and mutations for that particular module.
  • Run the project
  • Test and debug the integrated API using GraphiQL IDE

Client-Side Application:

Once your server-side is set up, it’s time to connect your application:

  • From the client-side application, Call API from a single endpoint (/graphql) and send the query string within the ‘data’ parameter as a request to get the response for that specified query string.
  • Get the response as JSON payload and process it for further execution.

Conclusion: 

GraphQL is a powerful tool that simplifies API development. By offering a more efficient and flexible approach to data retrieval, it empowers developers to build cleaner, faster, and future-proof applications.

Whether you’re a seasoned developer or just starting your journey, GraphQL is worth exploring. Its clear structure, streamlined data fetching, and ever-growing community make it a valuable asset for your development toolbox.

Additional Resources:

Folder Structure and Integration Reference: you can check the following link.

Video Reference on Integrations, you can check the following link.

About the Author:

Sudipta Mal is a Technical Architect at Mantra Labs. His passion goes beyond his technical expertise; he’s also fascinated by experimenting with new technologies, which further fuels his creativity and problem-solving abilities.

Further Readings: Beego is Backend Developers’ Fav for 2024; but why?

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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