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Using ReactJS With NodeJS | Ideal Tech Stack For High-Performance Web App Development

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Are you looking to create a full-stack web project? Are you overpowered by the options available in the market? 

Agreed, details like structure and code for the frontend and backend are requisite, but once you choose a library, you can integrate it with a popular framework to work on the development part. 

Are you aware that Node.js and React JS are the two most adopted technologies for web app development? 

React is perfect for the frontend, and Node.js is optimal for the backend. However, if you fuse the two frameworks, the result will be a seamless web app. 

What is React JS? 

You can create user interfaces with the React JS library. Also, you can use reusable components to make code simple to read and maintain. The best part is that these components auto-update when you interact with them. 

With that, you can expect a seamless response that works across many devices. As a result, your application is swift and responsive. React JS should be your first choice if you want to fasten your enterprise app development process. 

With React JS – you can build scalable online apps. Plus, it can help you build the best apps with the framework, even if you don’t know intricate scripting language. 

What is Node.js? 

If you need to build server-side and networking apps, your bet should be on Node.js. Apart from its sizable library of programming modules, you can facilitate the integration of programming languages with API and other third-party libraries. According to the statistics, 42.73% of Web programmers are using Node.js framework, libraries, and IDEs for their complex web development projects. 

Ideally, it runs on Chrome JavaScript runtime. You can build data-intensive real-time apps with Node.js because of its lightweight and efficient I/O model feature. Additionally, you can minimize the development time as it works on multiple platforms. 

Do you know most companies demand scalable software solutions today? Node.js will fit the scalability part as it handles concurrent requests efficiently. Also, the framework features cross-platform compatibility with rapid code execution. 

Going further, let’s look at some reasons to use React JS with Node.js. 

  • You can use Node.js for sequent server connection i.e. if your app’s core is based on real-time data streaming. 
  • You can use React JS and Node.js if you need to handle many requests and maintain server load balance. 
  • Also, you can develop single-page applications in React and use Node.js to build lightweight models for asynchronous data loading through the callback function.
  • You can fuse Node.js and React capabilities to build responsive data-driven multi-device apps. Basically, you can scale up your website performance. 
  • By integrating both technologies – you can expect more ROI and save added effort on app development. 

If you use React JS with Node.js, it can streamline the web development process. 

Here are some applied examples of using React JS with Node.js: 

Do you need to use the frameworks for standard web apps? You can use React JS to render the page and Node.js to handle the requests for the app. Ideally, for a chat app – you can use React JS to render the chat interface and Node.js can handle user communication. 

Steps to merge React JS with Node.js as a backend: 

Firstly, contrive a Node.js project 

You can contrive a Node.js project using a Node package manager (NPM). With this, you can set up a project structure at once. 

Now, set up the server 

It is time to contrive an express.js server – a flexible node.js web app framework. Ideally, you can install it as a dependency. 

Now, contrive a basic express server in a file i.e server.js 

const express = require(‘express’);

const app = express();

const port = 5000; // You can choose any port

app.get(‘/’, (req, res) => {

 res.send(‘Hello from the Node.js backend!’);

});

app.listen(port, () => {

 console.log(`Server is running on port number ${port}`);

});

It is time to initiate the Node.js server with:

node server.js

After that, your Node.js backend will run on your chosen port. 

Quick steps to merge React JS with Node.js as a frontend: 

You can contrive a React app using Create React App – a tool that sets up React projects quickly. 

npx create-react-app my-react-app

Now, you can fetch data from the backend 

You can use the React components to fetch API and request HTTP to the Node.js backend.

import React, { useState, useEffect } from ‘react’;

function App() {

 const [data, setData] = useState([]);

 useEffect(() => {

   // Fetch data from the backend

   fetch(‘/api/data’)

     .then((response) => response.json())

     .then((data) => {

       setData(data);

     });

 }, []);

 return (

   <div>

     <h1>Data from the Backend:</h1>

     <ul>

       {data.map((item, index) => (

         <li key={index}>{item}</li>

       ))}

     </ul>

   </div>

 );

}

export default App;

Now, connect to Node.js backend 

After the above step, you can connect to the Node.js backend by setting up a proxy in your package.json. 

“proxy”: “http://localhost:5000”

With this typical configuration – you can make backend requests without CORS issues. 

Finally, it is time to start your React development server 

cd my-react-app

npm start

As a last step, your React frontend is accessible at http://localhost:3000. In ideal case, you can fetch and display data from the Node.js backend.

Wrapping up

If you integrate React JS with Node.js, it can help you build present-day and scalable web applications. 

With React JS – you can arm the frontend user interfaces, and Node.js can arm serving data and business logic on the server side. 

Are you ready to contrive high-performance web apps with the two technologies? If scalability and flexibility are your foremost concerns, try using both frameworks for app development. 

About the Author: Harikrishna Kundariya, a marketer, developer, IoT, Cloud & AWS savvy, co-founder, and Director of eSparkBiz Technologies. His 12+ years of experience enables him to provide digital solutions to new start-ups based on IoT and SaaS applications.

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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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