How to Select the Right Serverless Architecture: A Quick Comparison?
Businesses are benefiting from serverless architecture. They pay only for the services they use and get greater agility in return. Operational costs get lowered and developers can code uninhibitedly without worrying about hardware resource allocation.
The biggest factor that works in favour of serverless architecture is the freedom to scale up and down as desired. All these pros make a strong business case for companies that are planning to shift their selected workloads to the serverless model.
If you have already migrated to this disruptive technology or are planning on doing so shortly, these battle-tested tips and hacks can help you greatly. Using these suggestions, you can find the right serverless architecture provider for your business and then chalk out a working model that gives you the best returns.
Which is the Best Serverless Provider for Me?
Your serverless provider will have long-term implications on your operations. The wrong selection can result in unimaginable loss in data, privacy, functionalities, and productivity.
There are many cloud computing providers who can help you set up your serverless architecture. But three platforms are undisputed leaders in the Function-as-a-service (FaaS) application model. They are:
Let us compare their salient features so that you can make an informed decision for your business.
Parameter #1: Language Support & Deployment Options
Azure AWS supports languages like C#, F#, Python, Java, Node.js, Python & PHP. It offers multiple deployment options, such as GitHub, DropBox, and OneDrive. You can also integrate it with Visual Studio and Zip Deployment.
Late entrant, Google Cloud, is supported by fewer languages, including Node.js 6, Node.js 7 and Python 3.7. More languages are expected to be appended in the list. For deployment, you have many options, such as Cloud Storage, Zip Upload, CLI, and inline web editor.
Parameter #2: Management of Dependency
In AWS Lambda, you have to organize and align your dependencies with your code in a specific way. In this way, you create a deployment package. For doing this, you can use plugins such as Jenkins and Maven.
Azure Functions has the package.json built into your function directory. You can run npm install using the Console or Kudu in the Azure portal. This process is used universally, regardless of your choice of runtime language.
Google Cloud Functions allows you to manage dependencies via npm and lets you create a metadata file called package.json. Unlike AWS, Google install all dependencies on the user’s behalf.
Parameter #3: Long-term Storage
The beauty of serverless architecture is that it is stateless. The functions written in this framework are not dependent on the underlying infrastructure and variables. AWS Lambda requires no use of variables while composing functions. But you can store variables in persistent locations like DynamoDB and cloud storage.
For Azure Functions, you may use Azure Blob Storage or Table Storage for storing persistent data across instances. In Google Cloud Functions, services such as Cloud Firestore, SQL, Storage, or Datastore should be used by your function is you want to share state.
Parameter #4: Access Management and Identity
Identity and Access Management (IAM) lets you control access to your functions by creating an authorization layer. Using this, you can decide what kind of resources you want (write-only or read-only) and what areas should these resources access (entire project or a single function only).
In AWS Lambda, you can create custom IAM policies and append to them your functions. Then, you can control users, roles, API actions, resources, and groups.
Azure Functions lets you create Resource Based Access Control for modulating function policies. Presently, you can grant read only/write only/ both rights.
Google Cloud Functions gives you more control as you can decide access permissions for each role.
Parameter #5: Types and Triggers
Triggers are custom events or sources that invoke functions. AWS Lambda allows HTTPS-invoked events using REST API. There is also an endpoint which uses API Gateway. You can also use the wide variety of built-in AWS services. Functions can also be set off via AWS SDKs if you have the requisite permissions.
In Azure Functions, you can use services such as Queue Storage, Table Storage, or Cosmos DB to create triggers. One add-on is that you can use external HTTP as well as Web Hooks for invocation.
Cloud Functions too supports HTTP triggers. You can also use Cloud Storage and Cloud Sub/Pub as triggers. New options are expected to be included in its Beta version.
Parameter #6: Orchestration
AWS has Step Functions that help to orchestrate serverless functions in a stateless architecture. Each function’s state is logged into a Step Function and used for subsequent functions.
In Azure Functions, Azure Logic Apps and Durable Functions accomplish the task of orchestration. Using connectors, you can integrate cloud services as well as on-site services. Google Cloud Functions suffers from a lack of orchestration capability.
The latest CNCF study reveals that AWS is the frontrunner among serverless architecture providers by claiming a market share of 70% deployments on Lambda. Its early advent and new updates are mostly responsible for its lead among competitors. But Google and Azure are fast catching up with their superb features and performance.
For newbies in serverless, our best advice is to give each provider a fair shot before making a final choice. You can evaluate each platform for compatibility with your custom software services, and choose the one which is best-aligned.
Get on a call with our expert to understand whether adopting serverless architecture for your web application is the right choice or not.
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