AWS Lambda

AWS Lambda automatically runs your code without requiring you to provision or manage infrastructure. Just write the code and upload it to Lambda either as a ZIP file or container image.

AWS Lambda automatically scales your application by running code in response to each event. Your code runs in parallel and processes each trigger individually, scaling precisely with the size of the workload, from a few requests per day, to hundreds of thousands per second.

With AWS Lambda, you only pay for the compute time you consume, so you’re never paying for over-provisioned infrastructure. You are charged for every millisecond your code executes and the number of times your code is triggered. With Compute Savings Plan, you can additionally save up to 17%.

With AWS Lambda, you can optimize your code execution time by choosing the right memory size for your function. You can also keep your functions initialized and hyper-ready to respond within double digit milliseconds by enabling Provisioned Concurrency.

You can use AWS Lambda to execute code in response to triggers such as changes in data, shifts in system state, or actions by users. Lambda can be directly triggered by AWS services such as S3, DynamoDB, Kinesis, SNS, and CloudWatch, can connect to existing EFS file systems, or it can be orchestrated into workflows by AWS Step Functions. This allows you to build a variety of real-time serverless data processing systems.

You can use Amazon S3 to trigger AWS Lambda to process data immediately after an upload. You can also connect to an existing Amazon EFS file system directly, which enables massively parallel shared access for large scale file processing. For example, you can use Lambda to thumbnail images, transcode videos, index files, process logs, validate content, and aggregate and filter data in real-time.

Reference Architecture: Sample Code

You can use AWS Lambda and Amazon Kinesis to process real-time streaming data for application activity tracking, transaction order processing, click stream analysis, data cleansing, metrics generation, log filtering, indexing, social media analysis, and IoT device data telemetry and metering.

Reference Architecture: Sample code

You can use AWS Lambda to preprocess data before feeding it to your machine learning model. With Lambda access to EFS, you can also serve your model for prediction at scale without having to provision or manage any infrastructure.

You can build serverless backends using AWS Lambda to handle web, mobile, Internet of Things (IoT), and 3rd party API requests. Take advantage of Lambda’s consistent performance controls, such as multiple memory configurations and Provisioned Concurrency, for building latency-sensitive applications at any scale.

By combining AWS Lambda with other AWS services, developers can build powerful web applications that automatically scale up and down and run in a highly available configuration across multiple data centers – with zero administrative effort required for scalability, back-ups or multi-data center redundancy.

Reference Architecture: Sample code

You can build serverless backends using AWS Lambda to handle web, mobile, Internet of Things (IoT), and 3rd party API requests.

Reference Architecture: Sample code

AWS Lambda makes it easy to create rich, personalized app experiences. You can build backends using AWS Lambda and Amazon API Gateway to authenticate and process API requests. Use AWS Amplify to easily integrate your backend with your iOS, Android, Web, and React Native frontends.

Reference Architecture: Sample code