AWS IoT Core is a managed cloud service that lets connected devices interact with cloud applications and other devices. It includes the Device Gateway and the Message Broker, which connect and process messages between IoT devices and the cloud. AWS IoT Core connects AWS and Amazon services like AWS Lambda, Amazon Kinesis, Amazon S3, Amazon SageMaker, Amazon DynamoDB, Amazon CloudWatch, AWS CloudTrail, Amazon QuickSight, and Alexa Voice Service to build IoT applications that gather, process,…
$0.08
Per Million Minutes
AWS Lambda
Score 8.3 out of 10
N/A
AWS Lambda is a serverless computing platform that lets users run code without provisioning or managing servers. With Lambda, users can run code for virtually any type of app or backend service—all with zero administration. It takes of requirements to run and scale code with high availability.
It turns out that AWS IoT Core is the most mature solution on the market with the best variety of integration tools available. On the downside, it is not the cheapest platform existing out there. Amazon IoT Core is easy to start and set up, and our prior engagement with Amazon …
For our use case, we ended up with AWS because the human resources that were planning to be resourced on this particular project happened to have prior familiarity with the AWS ecosystem. The conversation became can we justify continuing with this ecosystem rather than pivoting …
We really did not evaluate them against other products except a little Google research, we are a centralized AWS customer so it was a smooth and simple (even if blind) decision for us.
End-to-end encryption is an amazing feature because we use IoT to connect to various devices in order to gather data/ stats in real-time. We're able to publish solutions with ease and at a faster rate because of AWS IoT Core. However, its inability to interact with other IoT tools is a big con that I would like them to improve upon.
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.
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.
I give AWS IoT Core's overall usability this rating because it is very easy to use and is enjoyed by all of our staff. The only problem is that it sometimes glitches and it freezes a lot. So overall, the usability of AWS IoT Core is very good, and we will continue to use it.
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.
It covers all the aspects of IoT services required for an IoT company. It supports all the industry-wide protocols for secure data transmission and integrates powerful AL and ML technology for data analytics. For data storage, Amazon S3 is a great solution. Strong tech support and user community. Since it is widely used as compared to other products, there is an abundance of training and learning material on the web.
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.
Azure IoT service provides more or less the same services as compared to AWS IoT core, however the costing of AWS lead us to continued usage of IoT core over Azure IoT services. Also, considering our existing technology stack is on AWS, it was a natural selection for better integration and ease of use.
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
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.
