Serverless Developer Guide
Understanding serverless data processing
Processing data in serverless applications largely falls within the following three patterns:
• Asynchronous processing– big data processing, image/video manipulation, web hooks
• Synchronous processing – web apps, web services, microservices, web hooks
• Streaming – processing inbound data streams, from apps, IoT devices
The following topics provide a broad overview of each serverless processing pattern and explain 
the most common services you can use for each type. Use these topics to gain a conceptual 
understanding of serverless data processing on AWS.
Topics
• Asynchronous processing
• Synchronous processing
• Streaming
• Stateless data
Asynchronous processing
Serverless development allows your applications to ingest, process and analyze high volumes of 
data quickly and eﬃciently.
As the volume of data coming from increasingly diverse sources grows, you might ﬁnd you need 
to move quickly to process this data to ensure that your application's business logic can meet your 
needs. To process data at scale, organizations need to elastically provision resources to manage the 
information they receive from various microservices, mobile devices, operational data stores, and 
other sources.
Learn how to build a scalable serverless data processing solution. Use Amazon Simple Storage 
Service to trigger data processing or load machine learning (ML) models so that Lambda can 
perform ML inference in real time.
• File processing – Suppose you have a photo sharing application. People use your application 
to upload photos, and the application stores these user photos in an Amazon S3 bucket. Then, 
your application creates a thumbnail version of each user's photos and displays them on the 
Asynchronous processing 4