11 Top Programming Languages for IoT Beginners

In recent times, the Internet of Things is ubiquitous and is now a popular domain in the developer community. According to research by Statista, there are 6.21 million developers working in IoT and 5.36 million developers planning to work in IoT in next 6 months.

If you want to get started in IoT and are wondering which programming language to start with, here is a list of 11 popular programming languages used in IoT.

C

C, the language that was first developed to program telephone switches, is a reliable and reasonable choice for embedded system development. It is impressive because of its proximity to machine language.

It is a procedural language and the code is compiled and not interpreted. The code written in C is more reliable and scalable, and processor independence makes it a strong contender for IoT development. Because C is not platform independent, it enables IoT developers for code reuse, which can run on most of the systems.With the help of pointers, accessing and modifying addresses is easy in C.

C++

C++ is a middle-level programming language with imperative, object-oriented, and generic programming features with low-level memory manipulation.

C++ is designed with a bias toward system programming, embedded programming, resource-constrained devices and large systems. The design highlights of C++ are

Performance
Efficiency
Flexibility of use

C++ is a popular choice among the embedded developers coding for Linux systems. Linux programming expertise, particularly with C++, is crucial for developing efficient, scalable, and secure IoT application. Here are a few features that make C++ a popular choice among IoT developers:

Data hiding
Stronger typing/ checking
Multi-peripheral transparency using classes
Templates (as always if used carefully)
Initialisation lists

Go

Go is an open source programming language developed at Google. It combines the benefits of compiled language that is performance and security with that of aspeed of dynamic language.

It supports concurrent input, output, and processing on many different channels. Coordination of an entire fleet of sensors and actuators is possible when used correctly. The biggest risk is that the different channels don’t necessarily know about one another. If a programmer isn’t careful enough, a system could behave unpredictably because of a lack of coordination between channels.

In GO, gathering and sending data to various sensors and actuators is made easy by adding explicit hash table type.

The biggest advantage of GO is its ability to sort an entire network of sensors and making use of related IoT programming related devices. Go is now available on a wide variety of processors and platforms.

JavaScript

JavaScript is a scripting language with syntax similar to C. Initially, it was mainly used to create web pages, but now JavaScript is widely used in web servers, mobile apps, and IoT systems. JavaScript is good at event-driven applications; this allows every device to listen to various other events and respond to the concerned events. It has a garbage collector which eliminates freeing up of memory.

In your project, if you want to use the Apache server on a Raspberry Pi to collect data from a network of Arduino-based sensors, JavaScript would be good to start with.

Here are 10 reasons why JavaScript is used in IoT.

Python

The language, which was developed during a holiday break, went on to become the most preferred language for web development and started gaining popularity in embedded controls and IoT. Python is an interpreted language which can be either submitted for runtime compilation or run through one of theseveral pre-compilers so that compact executable code may be distributed.

The greatest benefit that Python offers to developers is readability with elegant syntax, without compromising on the size. Python’s clean syntax is apt for database arrangement. If your app requires data to be arranged in a database format and use tables for data control, Python is the best choice.

Python supports a huge number of libraries and modules, so you can get more stuff done with less code. It’s handy in more powerful edge devices, gateways, and also the cloud.

Rust

Rust is an open source, general-purpose, multi-paradigm, compiled programming language sponsored by Mozilla. Rust shares many of Go’s qualitiesand solves race condition problems of Go. Rust includes functions that eliminate race conditions for highly concurrent programs, making it a less risky language. Because of its ability to handle concurrent programming, Rust is now popular among IoT developers.

Rust is a safe, concurrent and practical language, supporting functional and imperative-procedural paradigms. It maintains these goals without a garbage collector. This makes Rust a useful language for the following use cases:

Embedding in other languages
Programming with specific space and time requirements
Writing low-level code, like device drivers and operating systems

Rust is an improvement on current languages by having a number of compile-time safety checks which produce no runtime overhead and eliminates all data races.

Java

Java is an object-oriented language, and there are very few hardware dependencies built into the compiler, which makes it incredibly portable.

The biggest concern in IoT is security; with the Generic Connection Framework 8 (GCF 8), the Access Point API in Java provides the latest security standards and the highest levels of networked encryption and authentication which ensure data privacy.

All the object references in Java are implicit pointers which cannot be manipulated by application code. This automatically rules out the potential risk of memory access violations which can inevitably cause an application to stop all of a sudden.

Connectivity at the application level of the IoT system is also easily handled in Java with a comprehensive set of APIs, both standard and freely available through open source projects.

Assembly language

Assembly language is a low-level programming language and is specific to a particular type of computer architecture. In contrast, to other high-level programming languages, Assembly language is not portable across multiple architectures.

Assembly language is also called symbolic machine code. It is converted into executable machine code by a utility program.

Assembly language is best if you want to make your IoT project compact, minimal, and optimal.

ParaSail

ParaSail stands for Parallel Specification and Implementation Language. It is a compiled, object-oriented language with syntax similar to Java, Python, C#, and Ada.

ParaSail is a language that you must consider if you have a requirement for parallel processing in your IoT system.

ParaSail is capable of both specifying and implementing parallel applications. It supports both implicit and explicit parallelism. Every ParaSail expression is defined to have parallel evaluation semantics.

R

R is a programming language and environment for statistical computing and graphics. It is widely used by statisticians and data miners for building statistical software and data analysis.

Here are a few statistical and graphical techniques implemented by R and its libraries:

Linear and nonlinear modeling
Time-series analysis
Classical statistical tests
Clustering
Classification and others

R is easily extensible through functions and extensions. It has stronger object-oriented programming facilities than most statistical computing languages. Extending R is also made easy with its lexical scoping rules.

B#

B# was designed as a very small and efficient embedded control language. The embedded virtual machine (EVM) that allows B# to run on a variety of different hardware platforms only takes 24 kb of memory. B# is lean enough for 8-bit MCUs.

The syntax of B# looks a bit like C#, with support for the real-time control functions that are critical to make things happen in the real world. The B# code when coupled with a compact virtual machine could be easily ported and reused across multiple hardware platforms.

B# supports writing portable interrupt handlers and device addressing registers in a uniform way.

It also supports modern object-oriented features such as

Namespaces
Abstract and concrete classes
Interfaces
Delegates

In addition to these, B# caters to the embedded systems programmer with

Efficient boxing/unboxing conversions
Multi-threading statements
Device addressing registers
Deterministic memory defragmenter
Field properties
Interrupt handlers

Each of these features is directly supported by the constructs of B# and its underlying virtual machine. This helps to create, use, and reuse more portable and decoupled software components across different embedded systems applications.

If your project is going to live on embedded hardware platforms that aren’t as big and complex as a Raspberry Pi, then B# is the best option available.

Forth

Forth has been around since 1970 and is designed and optimized for embedded system programming. It is an imperative stack-based language and environment. It includes features such as

Structured programming
Concatenative programming
Extensibility
Reflection

Forth features both interactive execution of commands and the ability to compile sequences of commands for later execution.

Forth is used in the Open Firmware boot loader, in space applications such as spacecrafts, and other embedded systems where interaction with hardware is involved.

HiveQL

HiveQL is based on SQL but does not strictly follow the full SQL-92 standard. It runs on the Hadoop map-reduce framework but hides the complexities from the developers.

HiveQL does not offer extensions in SQL, including multi-table inserts and create table as select. It only offers basic support for indexes. Here are few things which HiveQL can do easily:

Create and manage tables and partitions
Evaluate functions
Support various Relational, Arithmetic, and Logical Operators
Download the contents of a table to a local directory or result of queries to HDFS directory

Pig Latin

Pig Latin is the language of Apache Pig, which is a high-level platform for creating programs that run on Apache Hadoop. Pig Latin can be extended using User Defined Functions (UDFs) which you can write in Java, Python, JavaScript, Ruby, or Groovy and then call directly from the language.

Here are a couple of key properties of Pig Latin:

Ease of programming. With Pig Latin, it is easy to achieve parallel execution of simple and “embarrassingly parallel” data analysis tasks. Complex tasks with multiple interrelated data transformations are explicitly encoded as data flow sequences; this makes them easy to write, understand, and maintain.
Optimization opportunities. The way in which tasks are encoded permits the system to optimize their execution automatically, allowing you to focus on semantics rather than efficiency.

Extensibility. Users can create their own functions to do special-purpose processing.

Julia

Julia is a high-level, high-performance dynamic programming language for numerical analysis and computational science. Julia language provides

A sophisticated compiler
An extensive mathematical function library
Numerical accuracy
Distributed parallel execution

What makes Julia’s design unique is its type system with parametric polymorphism, and it types in a fully dynamic programming language. It also has multiple dispatch as its core programming paradigm. Julia allows concurrent, parallel, and distributed computing and direct calling of C and Fortran libraries without any glue code.

Julia’s LLVM-based JIT compiler combined with the language’s design allows it to often match the performance of C.

It does not impose any particular style of parallelism on the user. Instead, Julia provides flexibility to the user by providing a number of key building blocks for distributed computation through which it supports various styles of parallelism.

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Hackathons

Vibe Coding: Shaping the Future of Software

A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...

A New Era of Code

Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.

From Machine Language to Natural Language

I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.

The Promise and the Pitfalls

I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.

AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.

The Economic Impact

The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation.
Seizing the Opportunity

Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.

This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.

Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.

Tech Assessment

Guide to Conducting Successful System Design Interviews in 2025

What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...

What is Systems Design?

Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.

Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.

Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.

What is a System Design Interview?

Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:

How they navigate technical complexity and navigate uncertainty
How they meet expectations of scale, security and speed
How they focus on the bigger picture without losing sight of details

This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.

What are some common topics for a System Design Interview

System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:

How would you approach the design of a social media app or video app?

What are some ways to design a search engine or a ticketing system?

How would you design an API for a payment gateway?

What are some trade-offs and constraints you will consider while designing systems?

What is your rationale for taking a particular approach to problem solving?

Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.

For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.

The Difference between a System Design Interview and a Coding Interview

If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.

Here are three key difference between the two:

Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.

Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.

Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.

How to Conduct an Effective System Design Interview

One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview.
Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:

Step 1: Understand the subject at hand

Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty

Step 2: Prepare for the interview

Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise

Step 3: Stay actively involved

Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction

Step 4: Be a collaborator

Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience

Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.

Evaluation Rubric for Candidates

Facilitate Successful System Design Interview Experiences with FaceCode

FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.

FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.

With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.

Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.

AI Recruiting

How Candidates Use Technology to Cheat in Online Technical Assessments

Impact of Online Assessments in Technical Hiring In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers. The ease and...

Impact of Online Assessments in Technical Hiring

In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.

The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.

Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.

In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.

As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.

Cheating in Online Assessments is a High Stakes Problem

With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.

According to research, nearly 30 to 50 percent of candidates cheat in online assessments for entry level jobs. Even 10% of senior candidates have been reportedly caught cheating.

The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.

As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.

The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "

This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.

Common Cheating Tactics and How You Can Combat Them

Using ChatGPT and other AI tools to write code
Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub. Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
- Debugging code
- Optimizing existing code
- Writing problem-specific code from scratch
Ways to prevent it
- Enable full-screen mode
- Disable copy-and-paste functionality
- Restrict tab switching outside of code editors
- Use AI to detect code that has been copied and pasted
Enlist external help to complete the assessment

Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.

In extreme cases, some candidates might have an off-camera individual present in the same environment for help.

Ways to prevent it
- Verify a candidate using video authentication
- Restrict test access from specific IP addresses
- Use online proctoring by taking snapshots of the candidate periodically
- Use a 360 degree environment scan to ensure no unauthorized individual is present
Using multiple devices at the same time

Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.

By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.

Ways to prevent it
- Track mouse exit count to detect irregularities
- Detect when a new device or peripheral is connected
- Use network monitoring and scanning to detect any smart devices in proximity
- Conduct a virtual whiteboard interview to monitor movements and gestures
Using remote desktop software and virtual machines

Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.

Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.

With remote desktops, candidates can screen share the test window and use external help.

Ways to prevent it
- Restrict access to virtual machines
- AI-based proctoring for identifying malicious keystrokes
- Use smart browsers to block candidates from using VMs

Future-proof Your Online Assessments With HackerEarth

HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage. With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.

Secure, sealed-off testing environment
AI-enabled live test monitoring
Enterprise-grade, industry leading compliance
Built-in features to track, detect and flag cheating attempts

Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.

Top programming languages used in IoT

C

C++

Go