Amid everything going on, I stayed up after Day 2 of re:Invent to get my hands on the new Amazon S Q Digital Assistant. For more on the release here is the announcement. While sitting in the Adam Selipsky Keynote yesterday, a use case instantly popped into my head. The below example has no CDK or SAM but is purely a simple walkthrough of the functionality. Let me tell you about a time that Amazon Q met Taylor Swift.
I’ve been working recently with some data that doesn’t naturally fit into my AWS HealthLake datastore. I have some additional information captured in a DynamoDB table that would be useful to blend with HealthLake but on its own is not an FHIR resource. I pondered on this for a while and came up with the idea of piping DynamoDB stream changes to S3 so that I could then pick up with AWS Glue. In this article, I want to show you an approach to building a partitioned S3 bucket from DynamoDB. Refining that further with Glue jobs, tables and crawlers will come later.
When working on building solutions, the answer to some problems is often, it depends. For instance, if I need to deal with data as it changes and use DynamoDB, streams are the perfect feature to take advantage of. However, some data doesn’t need to be dealt with in real-time, once a day or every 30 minutes might be good enough. This was problematic up until recently, as AWS released incremental exports with DynamoDB. In this article, I want to explore building an incremental export with DynamoDB and Step Functions.
I’ve been wanting to spend more time lately talking about AWS HealthLake. And then more specifically, Fast Healthcare Interoperable Resources (FHIR) which is the foundation for interoperability in healthcare information systems. I believe very strongly that Serverless is for more than just client and user-driven workflows. I wrote extensively about it here but I wanted to take a deeper dive into building out streams of dataflows. I’ve been using this pattern for quite some time in production, so let’s have a look at EventBridge Pipes enriching DynamoDB Streams.
In my previous article I wrote about a Callback Pattern with AWS Step Functions built upon the backbone of HealthLake’s export. As much as I went deep with code on the Callback portion, I felt that I didn’t give the HealthLake side of the equation enough run. So this article is that adjustment. Managing exports with AWS HealthLake.
What is HealthLake
AWS HealthLake is a HIPAA-eligible service that provides FHIR APIs that help healthcare and life sciences companies securely store, transform, transact, and analyze health data in minutes to give a chronological view at the patient and population-level. – AWS
My words on that are that HealthLake is a FHIR-compliant database that gives a developer a robust set of APIs to build patient-centered applications. You can use HealthLake for building transactional applications, analyze large volumes of data, store structured and semi-structured information and build analytics and reports.
When building with HealthLake I find it fits in one of two places.
- As the transactional center for your Healthcare application. It is highly patient centered, very scalable and contains APIs for working with each resource. In addition, it provides SMART on FHIR capabilities that make it nice choice for building an application on top of.
- As the aggregation point for many external and internal systems in a LakeHouse style architecture for interopability and reporting. When you’ve got a distributed system with various datababases and you need your data reunited in one location. HealthLake does that. Or if you are pulling in data from various external sources, HealthLake can do that too. I wrote about doing this with Serverless a while back.