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NodeJS vs Java vs Python

4 minutes, 57 seconds read

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:

SimplicityMediumVery SimpleSimple


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.


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.


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.


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. 


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.


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.


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.


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.


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.


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Retention playbook for Insurance firms in the backdrop of financial crises

4 minutes read

Belonging to one of the oldest industries in the world, Insurance companies have weathered multiple calamities over the years and have proven themselves to be resilient entities that can truly stand the test of time. Today, however, the industry faces some of its toughest trials yet. Technology has fundamentally changed what it means to be an insurer and the cumulative effects of the pandemic coupled with a weak global economic output have impacted the industry in ways both good and bad.

Chart, line chart

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Source: Deloitte Services LP Economic Analysis

For instance, the U.S market recorded a sharp dip in GDP in the wake of the pandemic and it was expected that the economy would bounce back bringing with it a resurgent demand for all products (including insurance) across the board. It must be noted that the outlook toward insurance products changed as a result of the pandemic. Life insurance products were no longer an afterthought, although profitability in this segment declined over the years. Property-and-Casualty (P&C) insurance, especially motor insurance, continued to be a strong driver, while health insurance proved to be the fastest-growing segment with robust demand from different geographies

Simultaneously, the insurance industry finds itself on the cusp of an industry-wide shift as technology is starting to play a greater role in core operations. In particular, technologies such as AI, AR, and VR are being deployed extensively to retain customers amidst this technological and economic upheaval.

Double down on digital

For insurance firms, IT budgets were almost exclusively dedicated to maintaining legacy systems, but with the rise of InsurTech, it is imperative that firms start dedicating more of their budgets towards developing advanced capabilities such as predictive analytics, AI-driven offerings, etc. Insurance has long been an industry that makes extensive use of complex statistical and mathematical models to guide pricing and product development strategies. By incorporating the latest technological advances with the rich data they have accumulated over the years, insurance firms are poised to emerge stronger and more competitive than ever.

Using AI to curate a bespoke customer experience

Insurance has always been a low-margin affair and success in the business is primarily a function of selling the right products to the right people and reducing churn as much as possible. This is particularly important as customer retention is normally conceived as an afterthought in most industries, as evidenced in the following chart.

Chart, sunburst chart

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        Source: econconusltancy.com

AI-powered tools (even with narrow capabilities) can do wonders for the insurance industry at large. When architected in the right manner, they can be used to automate a bulk of the standardized and automated processes that insurance companies have. AI can be used to automate and accelerate claims, assess homeowner policies via drones, and facilitate richer customer experiences through sophisticated chatbots. Such advances have a domino effect of increasing CSAT scores, boosting retention rates, reducing CACs, and ultimately improving profitability by as much as 95%.

Crafting immersive products through AR/VR

Customer retention is largely a function of how good a product is, and how effective it is in solving the customers’ pain points. In the face of increasing commodification, insurance companies that go the extra mile to make the buying process more immersive and engaging can gain a definite edge over competitors.

Globally, companies are flocking to implement AR/VR into their customer engagement strategies as it allows them to better several aspects of the customer journey in one fell swoop. Relationship building, product visualization, and highly personalized products are some of the benefits that AR/VR confers to its wielders.  

By honoring the customer sentiments of today and applying a slick AR/VR-powered veneer over its existing product layer, insurance companies can cater to a younger audience (Gen Z) by educating them about insurance products and tailoring digital delivery experiences. This could pay off in the long run by building a large customer base that could be retained and served for a much longer period.

The way forward

The Insurance industry is undergoing a shift of tectonic proportions as an older generation makes way for a new and younger one that has little to no perceptions about the industry. By investing in next-generation technologies such as AR/VR, firms can build new products to capture this new market and catapult themselves to leadership positions simply by way of keeping up with the times.

We have already seen how AR is a potential game-changer for the insurance industry. It is only a matter of time before it becomes commonplace.


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