Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(6)

Manufacturing(5)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(41)

Insurtech(67)

Product Innovation(59)

Solutions(22)

E-health(12)

HealthTech(25)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(154)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(24)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

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?

Cancel

Knowledge thats worth delivered in your inbox

Smart Manufacturing Dashboards: A Real-Time Guide for Data-Driven Ops

Smart Manufacturing starts with real-time visibility.

Manufacturing companies today generate data by the second through sensors, machines, ERP systems, and MES platforms. But without real-time insights, even the most advanced production lines are essentially flying blind.

Manufacturers are implementing real-time dashboards that serve as control towers for their daily operations, enabling them to shift from reactive to proactive decision-making. These tools are essential to the evolution of Smart Manufacturing, where connected systems, automation, and intelligent analytics come together to drive measurable impact.

Data is available, but what’s missing is timely action.

For many plant leaders and COOs, one challenge persists: operational data is dispersed throughout systems, delayed, or hidden in spreadsheets. And this delay turns into a liability.

Real-time dashboards help uncover critical answers:

  • What caused downtime during last night’s shift?
  • Was there a delay in maintenance response?
  • Did a specific inventory threshold trigger a quality issue?

By converting raw inputs into real-time manufacturing analytics, dashboards make operational intelligence accessible to operators, supervisors, and leadership alike, enabling teams to anticipate problems rather than react to them.

1. Why Static Reports Fall Short

  • Reports often arrive late—after downtime, delays, or defects have occurred.
  • Disconnected data across ERP, MES, and sensors limits cross-functional insights.
  • Static formats lack embedded logic for proactive decision support.

2. What Real-Time Dashboards Enable

Line performance and downtime trends
Track OEE in real time and identify underperforming lines.

Predictive maintenance alerts
Utilize historical and sensor data to identify potential part failures in advance.

Inventory heat maps & reorder thresholds
Anticipate stockouts or overstocks based on dynamic reorder points.

Quality metrics linked to operator actions
Isolate shifts or procedures correlated with spikes in defects or rework.

These insights allow production teams to drive day-to-day operations in line with Smart Manufacturing principles.

3. Dashboards That Drive Action

Role-based dashboards
Dashboards can be configured for machine operators, shift supervisors, and plant managers, each with a tailored view of KPIs.

Embedded alerts and nudges
Real-time prompts, like “Line 4 below efficiency threshold for 15+ minutes,” reduce response times and minimize disruptions.

Cross-functional drill-downs
Teams can identify root causes more quickly because users can move from plant-wide overviews to detailed machine-level data in seconds.

4. What Powers These Dashboards

Data lakehouse integration
Unified access to ERP, MES, IoT sensor, and QA systems—ensuring reliable and timely manufacturing analytics.

ETL pipelines
Real-time data ingestion from high-frequency sources with minimal latency.

Visualization tools
Custom builds using Power BI, or customized solutions designed for frontline usability and operational impact.

Smart Manufacturing in Action: Reducing Market Response Time from 48 Hours to 30 Minutes

Mantra Labs partnered with a North American die-casting manufacturer to unify its operational data into a real-time dashboard. Fragmented data, manual reporting, delayed pricing decisions, and inconsistent data quality hindered operational efficiency and strategic decision-making.

Tech Enablement:

  • Centralized Data Hub with real-time access to critical business insights.
  • Automated report generation with data ingestion and processing.
  • Accurate price modeling with real-time visibility into metal price trends, cost impacts, and customer-specific pricing scenarios. 
  • Proactive market analysis with intuitive Power BI dashboards and reports.

Business Outcomes:

  • Faster response to machine alerts
  • Quality incidents traced to specific operator workflows
  • 4X faster access to insights led to improved inventory optimization.

As this case shows, real-time dashboards are not just operational tools—they’re strategic enablers. 

(Learn More: Powering the Future of Metal Manufacturing with Data Engineering)

Key Takeaways: Smart Manufacturing Dashboards at a Glance

AspectWhat You Should Know
1. Why Static Reports Fall ShortDelayed insights after issues occur
Disconnected systems (ERP, MES, sensors)
No real-time alerts or embedded decision logic
2. What Real-Time Dashboards EnableTrack OEE and downtime in real-time
Predictive maintenance using sensor data
Dynamic inventory heat maps
Quality linked to operators
3. Dashboards That Drive ActionRole-based views (operator to CEO)
Embedded alerts like “Line 4 down for 15+ mins”
Drilldowns from plant-level to machine-level
4. What Powers These DashboardsUnified Data Lakehouse (ERP + IoT + MES)
Real-time ETL pipelines
Power BI or custom dashboards built for frontline usability

Conclusion

Smart Manufacturing dashboards aren’t just analytics tools—they’re productivity engines. Dashboards that deliver real-time insight empower frontline teams to make faster, better decisions—whether it’s adjusting production schedules, triggering preventive maintenance, or responding to inventory fluctuations.

Explore how Mantra Labs can help you unlock operations intelligence that’s actually usable.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot