Join David Linthicum for an in-depth discussion in this video AWS Lambda, part of Learning Cloud Computing: Serverless Computing.
- [Instructor] Let's talk about AWS Lambda. What's important to remember about this serverless product is, number one, it's the most used cloud computing serverless platform out there. So we're gonna talk about Microsoft functions, and Google has a version of a serverless platform, as well, and you can count on it being a part of Alibaba cloud, and some other public cloud providers that are out there. They offer, basically, the same pattern of service, but they do so in different ways. Most of the other cloud providers use Lambda as a standard bearer of serverless computing.
So keep in mind that, ultimately, when you'd look at AWS Lambda, it really becomes, kind of, the prototype for the other serverless platforms out there. That may not always be the case, but it is for now. So we have AWS Lambda, which is surrounded by the code, and it's able to use Node.js, Java, Python, or you can bring your own libraries, even native ones. So, in other words, it's very open in the ways in which you can build applications within Lambda.
Second, the resource model. So you can select power rating from 128 megabytes to 1.5 gigabytes. CPU and network allocated proportionally and reports actual usage. So, keep in mind that we can basically select the power rating, which means the way in which Lambda is gonna size our servers up and run our applications. The CPU and the network bandwidth that you need, and the CPU power is allocated proportionally to what the application needs. So, in other words, it looks at what the applications requirements are, and it allocates the appropriate resources for the application, and it only reports actual usage.
So, we get a bill for the utilization of AWS Lambda serverless platform, and we're only gonna pay for what we use. It's not like the traditional cloud computing model, or we're allocating resources, and we have to pay for those resources no matter how long we use them, no matter how less that we use them, ultimately, if we're allocating them, they have to be paid for. As far as the usage model, it's able to call or send events. It's integrated with other AWS services, and so you can leverage the queuing system, you can leverage the databases, you can leverage Kineses, you can leverage all sorts of services you may use.
Cloud native services that are a part of AWS. So this is integrated, of course, within the AWS platform, this is not designed to standalone onto itself, we can build applications that are able to leverage these native cloud computing platform resources. We can build a whole serverless ecosystem using this technology. We can build as many functions as we need. We can bind them together. We can orchestrate the functions, and then, finally, security. We're able to securely grant access to resources including VPC's, virtual private clouds, and it's able to do fine-grained control over who can call your functions, and so, in other words, it's not enough to create security around entire applications.
We actually create security around the functions that it's able to build, and we have governance capabilities to, in essence, through an identity access management, kind of, a paradigm, talk to people who can allocate the functions, and leverage the functions, and they're the only ones who are authorized to do it. So in other words, when you build a function, you can authorize yourself to invoke the functions, but you can also authorize other users, and also other machines, as are identified within the AWS ecosystem, that's able to, in essence, drive as much fine-grained security into the applications as you need, and so, in some instances, we want to have people who have access to all the functions that are part of an application, but some of the functions, for example, some that may deal with personal, identifiable information, we want to allocate that only to a select few.
This provides you the capability of doing that.
- What is serverless computing?
- Use cases
- Platforms: AWS Lambda and Azure Functions
- Planning and building applications
- Steps to deployment