In this workshop, you will explore several patterns for stream processing at scale in AWS. The methods used here are based on the Serverless Streaming and Architecture Best Practices White Paper.
Imagine that you are at a health care company that deploys equipment in hospitals across the globe. The equipment collects patient data, and you need to ingest and analyze the data at scale, in near real-time, in a HIPAA compliant manner.
In this workshop, you will deploy and explore the architecture below, which takes in simulated life support device sensor data and processes it. The entire architecture is serverless and uses HIPAA-eligable services.
This workshop is divided into three modules. You must complete Set Up before starting any of the other modules.
| Module | Description | |
|---|---|---|
| 0 | Set Up | Deploy the architecture using a CloudFormation template. |
| 1 | Collect & De-Identify Data | Ingest simulated real-time device data into IoT Core, de-identify the data using an IoT Lambda Action, saving the PHI/PII data to an encrypted DynamoDB table while sending PHI/PII-free data on to S3 via Kinesis Firehose. |
| 2 | Enrich Data | Enrich streaming data using a Kinesis Firehose Record Transformation with metadata from DynamoDB, and then store the enriched records in S3. |
| 3 | Detect Anomalies | Use Kinesis Analytics to calculate anomaly scores with the Random Cut Forest algorithm, and automatically send an SNS text message if an anomaly is found. |