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.
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gcloud CLI
Score 8.7 out of 10
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The Google Cloud CLI is a set of tools to create and manage Google Cloud resources. Its tools can be used to perform many common platform tasks from the command line or through scripts and other automation.
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1024 MB
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10240 MB
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AWS Lambda
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AWS Lambda
Google Cloud CLI
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Comparison of Access Control and Security features of Product A and Product B
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.
it is great for automating tasks like deploying VMs, running BigQuery and managing cloud resources at scale. It is much faster than the UI for repetitive actions and allows you to easily script and scale progress. However it is not ideal for beginners
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 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.
Google Cloud CLI is highly usable once you’re past the basics, especially for engineers and DevOps teams who live in the terminal. It’s powerful, consistent, and scriptable, which makes it excellent for repeatable workflows, automation, and day-to-day cloud operations. The command structure is generally logical, tab-completion and help flags are solid, and it integrates cleanly into CI/CD pipelines—huge wins for productivity
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.
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
Google Cloud CLI stacks up well against other cloud and infrastructure CLIs by balancing power, consistency, and automation readiness, especially in production environments. Compared to AWS CLI, it’s more opinionated and readable, with better defaults and a more coherent command structure across services. Compared to Azure CLI, it’s less beginner-friendly but offers deeper control and is better suited for complex, large-scale workflows.
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.
