Category: Healthcare

In this post, we discuss how Phreesia used AWS DMS to replicate their on-premises database to AWS in an effective and cost-optimized manner. Because of the database’s large size and complex data structure, properly tuning the AWS DMS configuration was critical to minimize the migration duration and cost. We outline the fine-tuning techniques that were applied to optimize the AWS DMS task settings, instance size, IOPS provisioning, and table mappings. Applying these performance optimizations allowed Phreesia to develop a migration strategy to move this 30 TB database to Amazon S3 in just 2 days without disruption to production workloads.

Blockchain technology has the potential to revolutionize how personal digital records are managed, stored, and shared, because it offers unique features such as immutability, transparency, security, and decentralization. The application possibilities of blockchain technology in the context of personal digital records encompass various potential use cases, including but not limited to: Create decentralized digital identity […]

Modeling chemical structures can be a complex and tedious process, even with the help of modern programs and technology. The ability to explore chemical structures at the most fundamental level of atoms and the bonds that connect them is an essential process in drug discovery, pharmaceutical research, and chemical engineering. By infusing chemical research with […]

In this post we focus on data analysis as part of the modern data strategy. I cover how to generate insights from healthcare FHIR (Fast Healthcare Interoperability Resources) data with Amazon Neptune, a fast, reliable, fully managed graph database service. Using a graph database for this use case allows you to model and navigate complex […]