Table of Contents
Serverless architecture is a very powerful approach to software design as it allows developers to build and run services without having to manage underlying infrastructure. Cloud providers are responsible for provisioning underlying infrastructure and developers can write and deploy code. Cloud providers shall provide resources to run their applications, databases and storage systems at any scale.
Today we look more in detail about how serverless architectures for big three cloud providers work – AWS Lambda, Azure functions and Google cloud functions , its key differences and use cases.
Use cases of AWS Lambda
- Scalable APIs can be created using AWS Lambda as it automatically scales individual functions according to demand from them
- Data processing in Lambda is optimized for event-based data processing and can be used to trigger a Lambda function for specific event of data
- AWS Lambda is good candidate for task automation and can help to automate tasks that do not require entire server at all times
Also Read: AWS Fargate vs AWS Lambda
Azure functions was created in year 2016 and allows functions to be coded in its native languages – C# and F# inside a web function editor or can be written and uploaded using common scripts-based options such as Bash, Batch and PowerShell. It has out of box integration with VS teams services, GitHub, and Bitbucket.
Use cases for Azure functions
- Data can be processed by incoming batches and multiple format consumed by functions which can perform multiple operations on data such as conversion, filtering, cleaning etc.
- Integration of systems can be done via functions of Azure and legacy systems and code can be integrated
- IoT is biggest candidate for functions in today’s applications
- Simple APIs and Microservices – Legacy monolithic code can be broken down into smaller discrete function triggered with events and functions can be connected
Google Cloud Functions
Use cases for Google functions
- Serverless application backends to trigger code from Google cloud services or call it from any mobile or web application
- Real time data processing systems where code can be executed based on changes in data so functions can respond to events
- Intelligent applications to extend products and services with voice and text based natural experiences to get things done for users
AWS Lambda vs Azure Functions vs Google Cloud Functions
Below table gives a detailed comparison of these three:
Market share – AWS Lambda (0.18%), Azure functions (0.08%) and Google functions (0.76%)