AWS Lambda vs. Microsoft Azure IoT Hub

Lambda excels at event-driven, short-lived tasks, such as processing files or building simple APIs. However, it's less ideal for long-running, computationally intensive, or applications that rely on carrying the state between jobs. Cold starts and constant load can easily balloon the costs.

Incentivized

We are using the Azure IOT hub for solving the multitenancy problem within our research project. We are consuming data from various resources and communicating it with different devices on our hybrid cloud. We also use Azure IoT as a bridge between two business Intelligence sources which are really hard to connect devices.

Incentivized

• No provisioning required - we don't have to pay anything upfront
• Serverless deployment - it gets executed only when request comes and we pay only for the time the request is getting executed
• Integrates well with AWS CloudWatch triggers so it is easy to setup scheduled tasks like cron jobs

Incentivized

• Massive registration and deployment of new devices.
• Easy integration with other Azure services.
• Handling security if devices.

Incentivized

• Developing test cases for Lambda functions can be difficult. For functions that require some sort of input it can be tough to develop the proper payload and event for a test.
• For the uninitiated, deploying functions with Infrastructure as Code tools can be a challenging undertaking.
• Logging the output of a function feels disjointed from running the function in the console. A tighter integration with operational logging would be appreciated, perhaps being able to view function logs from the Lambda console instead of having to navigate over to CloudWatch.
• Sometimes its difficult to determine the correct permissions needed for Lambda execution from other AWS services.

Incentivized

• Hardware and embedded integration could improve or be at parity with competitor.
• Pricing model not as flexible as competitor.

Incentivized

I give it a seven is usability because it's AWS. Their UI's are always clunkier than the competition and their documentation is rather cumbersome. There's SO MUCH to dig through and it's a gamble if you actually end up finding the corresponding info if it will actually help. Like I said before, going to google with a specific problem is likely a better route because AWS is quite ubiquitous and chances are you're not the first to encounter the problem. That being said, using SAM (Serverless application model) and it's SAM Local environment makes running local instances of your Lambdas in dev environments painless and quite fun. Using Nodejs + Lambda + SAM Local + VS Code debugger = AWESOME.

Incentivized

Amazon consistently provides comprehensive and easy-to-parse documentation of all AWS features and services. Most development team members find what they need with a quick internet search of the AWS documentation available online. If you need advanced support, though, you might need to engage an AWS engineer, and that could be an unexpected (or unwelcome) expense.

Incentivized

Azure IoT support professionals are strong, and always provide timely responses. Vast documentation and examples are available, plus a network of professionals in the market. It's very comparable to the main competitor offer, and easily integrated into the main Azure product offer. Azure IoT is not a new solution, so it is very mature and support can easily address any day to day or architectural concern you have.

Incentivized

AWS Lambda is good for short running functions, and ideally in response to events within AWS. Google App Engine is a more robust environment which can have complex code running for long periods of time, and across more than one instance of hardware. Google App Engine allows for both front-end and back-end infrastructure, while AWS Lambda is only for small back-end functions

Incentivized

We require Azure integration, data security and simple development. IoT Hub ticked all those boxes.

Incentivized

• Positive - Only paying for when code is run, unlike virtual machines where you pay always regardless of processing power usage.
• Positive - Scalability and accommodating larger amounts of demand is much cheaper. Instead of scaling up virtual machines and increasing the prices you pay for that, you are just increasing the number of times your lambda function is run.
• Negative - Debugging/troubleshooting, and developing for lambda functions take a bit more time to get used to, and migrating code from virtual machines and normal processes to Lambda functions can take a bit of time.

Incentivized

• As a student, we are using student program subscription which is cheaper than an individual or business subscription.
• Our school devices our efficiently communicating with each other which has increased the revenue of our school.
• It provides strong and robust cloud communication within the premises which also helped in increased in the school reputation.

Incentivized