Common reference architectures we use

We follow standard industry architectures whenever possible to make our software development processes more predictable and repeatable. This also makes our solutions easier to support once they are built. These solutions share best practice patterns often applied by engineers and architects across many industries.

Data Lake

A scalable data lake architecture provides your organization with a solid foundation to gain value from your data lake while bringing more data into it. By continuously gaining data insights without being slowed down or interrupted because of scalability constraints, a scalable data lake also helps your organization remain competitive.

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Health Data Lake

Amazon HealthLake is a HIPAA-eligible service offering healthcare and life sciences companies a complete view of individual or patient population health data for query and analytics at scale.

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Data Lake House

As a modern data architecture, the Lake House approach is not just about integrating your data lake and your data warehouse, but it’s about connecting your data lake, your data warehouse, and all your other purpose-built services into a coherent whole.

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Data Producers and Consumers

Typically, a data lake has data producers and data consumers. Data producers create data assets by collecting, processing, and storing data from their data domain. These collective data assets form the content of your data lake. Data producers can choose to selectively share their data assets with the data lake's data consumers.
Data consumers need the data from the data producers to fulfill their business use cases and can also occasionally combine this data with their own data. Data producers and data consumers are typically, but not always, part of your organization. Importantly, they can both be data producers or data consumers at the same time.
A scalable data lake architecture helps you to achieve the following outcomes:

Data producers

Onboard data producers at scale without requiring them to maintain the entire data sharing process. This helps data producers onboard their data into the data lake and focus on collecting, processing, and storing data from their data domain.

Data consumers

Enable data consumers to access data from multiple data producers without increasing your overall costs and management overhead.

heatlh data LAke

AWS HealthLake

Natural language processing

Extract meaning from unstructured data with natural language processing (NLP) for easy search and querying.


Make predictions with health data using Amazon SageMaker machine learning (ML) models and Amazon QuickSight analytics.


Support interoperable standards such as the Fast Healthcare Interoperability Resources (FHIR) format.

Complete view

Create a complete and chronological view of patient health data, including prescriptions, procedures, and diagnoses.

DATA LAke house

The single place for analytics

The Data Lake House allows you to have a single place you can run analytics across most of your data while the purpose-built analytics services provide the speed you need for specific use cases like real-time dashboards and log analytics.

This Lake House approach consists of the following key elements:

Scalable data lakes

Purpose-built Data Services

Seamless Data Movement

Unified Governance

Performant and Cost-effective Patterns and Processes

A layered data analytics architecture enables you to use the right tool for the right job. You gain the flexibility to evolve your Lake House to meet current and future needs as you add new data sources, discover new use cases, and develop new analytics methods.For this Lake House Architecture, you can organize it as a stack of five logical layers, where each layer is composed of multiple purpose-built components that address specific requirements. We can customize this stack for your needs.