4 minutes, 57 seconds read

Published on May 23, 2022

The evolution of the language or tool depends on the problem statement and advancement of hardware.With the emergence of cloud computing few languages like Java, PHP, .NET, Python, JS and their respective tool sets are in trend. In this article we shall concentrate on three technologies i.e Java, Node JS and Python and see a comparative study of them.

The internal workings

Here I want to present the working principle of the three. One thing is clear, Java is the only compiled language but Node JS and Python are interpreted languages.

Working Principle of Java, NodeJs, Python

For beginners that may not be a big deal, but this may change the whole discourse. When we compile the code, it is ready to consume by the hardware but when it’s interpreted the code is converted to byte code on the runtime, it may turn out to 10X performance improvement depending upon the situation.

Following is the table which will depict execution time, CPU, memory utilization and the code size for some standard algorithms. Credit goes to benchmarksgame-team. For details of the unit, you can refer here.

Algorithm Comparison Table: NodeJs vs Java vs Python

The following table depicts the comparison between on the basis of speed, performance, scalability and more:

Parameters	NodeJs	Python	Java
Speed	Faster	Fast	Fastest
Performance	Low	High	High
Scalability	Highest	Medium	High
Simplicity	Medium	Very Simple	Simple
Community	Strong	Strong	Strong
Library	Excellent	Good	Good
Cost	Free	Free	Paid
Cross-functionality	High	High	High

Speed

As Java is compiled as bytecode and statically linked code the performance is always faster, in most of the cases ten times faster than the other two. There are a few odd cases where Java falls short of speed. In those cases, it boils down to mismatched use cases, legacy code, and wrong coding practices.

NodeJs speed is better than Python thanks to the V8 engine. The V8 engine interprets the javascript code to machine language and optimizes the solution to reduce load time. NodeJs programs run on a single thread. However, you can easily find multi-threaded libraries. The libraries were used to create a thread pool and used multiple CPU cores simultaneously in the background.

Performance

Computer performance is the amount of useful work accomplished by the computer system. So the performance of a system depends on the right kind of technology picked for a particular workload. Java naturally supports multithreading hence if an application does heavy parallel processing, it will be really a great choice. If an application makes lots of networks, it calls Node JS which will be the winner as it naturally supports event-driven programming and hence asynchronous programming. Python is mostly evolving as a middle ground to achieve a decent performance and it always has the advantage of being a simple language to learn.

Scalability

Looking at the current evolution of cloud infrastructure, to achieve scalability using infrastructure tricks for stateless web applications is a norm. The real challenge is to scale a stateful application. The scalability depends on the purpose of the application and the technology we pick.

Node.js is quite scalable, owing to microservices, event-driven architecture, and non-blocking I/O. It allows the creation of microservices and modules. Whenever the solution expands, these microservices and modules resort to dynamic process runs and keep the performance and speed in check.

Java being garbage collected by the resource optimized JVM, it becomes a decent choice to scale.

Python is hard to scale as it’s dynamically typed it’s always slower. As the code goes the system also gets slower and the system gets too tangled.

Simplicity

It is measured as the amount of time one needs to spend learning the language and using it. So it boils down to the familiarity with syntax, expressions and concepts. Also with ease, a developer adapts an existing project and starts contributing.

Java is object-oriented programming and memory management is taken care of by the JVM hence its learning curve is small.

Python on the other hand is a high-level language and its syntax is more intuitive. Hence the learning curve is even smaller than Java and that is definitely the factor used in most non-software industries like data science and others.

The learning curve of the NodeJs is simple too, but the inner workings of the run time environment like async programming, hook, and patterns are difficult to grasp.

Community

All of them established themself in their own markets. Both Java and Python have been around for quite a long time and have healthy communities. NodeJs is a relatively new technology still looking at the adaptation and as its open-source, it has a sizable community.

Library

All three have a voluminous library to support various functions and they are well documented.

When working with NodeJs, you will find NPM (NodeJs Package Manager.) It is a free online repository that fuels and simplifies JavaScript development by storing NodeJs packages.

Cost

Python comes with lots of open source libraries and frameworks that help to reduce the cost of python. Whereas Java is now owned by Oracle and it’s licensed and to get the support we need to pay the license cost. The cost involved for NodeJs using the NPM packages is cost-free, there will be a cost involved for the paid library for payment gateway and third-party integration.

Cross-Functional

All of the above work seamlessly across different environments. As Java is meant for code once and it will run everywhere hence it’s suitable for network application, parallel processing, and web application development. Python can easily run as far as the runtime remains the same, it’s suitable for web applications and data science applications. NodeJs works for multiple OS and devices hence it’s good for web applications and cloud-based IoT solutions.

Conclusion

There is no winner or loser in these comparisons, many factors depend on the tools or language that we use, it depends on the problem we are resolving, the performance criteria, the compatibility to the existing framework and toolsets. Finally the learning curve of the team who will use this.

About the author:

Manoj is Solution Architect at Mantra Labs working on cloud native solutions. He loves to follow emerging trends in Software technology. Currently, he is working on Cloud Native tools and technologies.

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.

NodeJS vs Java vs Python