Data Lakehouse, Data Warehouse, and Data Mart: What's the Difference?

In Microsoft Fabric, the Lakehouse, Data Warehouse, and Power BI Datamart are similar objects that offer data storage, ETL loading, and Power BI reporting capabilities.

Today, we will outline their distinctions and guide you in selecting the optimal option for your implementation and architecture.

Data Lakehouse

A data lakehouse is a central repository that stores raw, structured, semi-structured, and unstructured data in its native format. It leverages the scalability and cost-effectiveness of data lakes while providing transactional capabilities, schema enforcement, and query optimization found in data warehouses. The data lakehouse architecture would allow data engineers to explore and analyze this diverse data for insights, predictive modeling, and advanced analytics.

Such data can be EHR data, sensor data from medical devices, patient-generated data from wearable devices, and textual data from clinical notes.

Data Warehouse

A data warehouse is a large, centralized repository of integrated and historical data from various sources within an organization. They are structured to facilitate data consolidation, transformation, and aggregation, providing a single source of truth for decision-making. They allow database developers to support business intelligence, reporting, and data analysis activities.

Examples of data stored in a data warehouse could include quality metrics, financial data, clinical data, and hospital operational data.

Data Marts

A data mart is a subset of a data warehouse that focuses on a specific functional area or business unit within an organization. It contains a condensed and specialized set of data that is optimized for a particular business function, such as patient satisfaction. They allow data analysts to provide quick and efficient access to relevant data for analysis and reporting purposes within a specific department or team.

Examples of data that can be included in a data mart for patient satisfaction purposes could be patient feedback, staff responsiveness, patient complaints, and wait times.

Conclusion:

When choosing between these options, it's important to consider the specific needs of your organization. If you require a consolidated view of data from different sources for enterprise-wide reporting and analysis, a data warehouse would be a suitable choice. However, if you deal with large volumes of raw and diverse data that require exploration and real-time analytics, a data lakehouse can offer the necessary scalability and flexibility. Data marts, on the other hand, cater to specific departmental needs and can provide focused insights for decision-making.