Data Lake, Data Warehouse, Datamart, and Feature Store: Their Contributions to the Complete Data Reuse Pipeline

Area of Science:

• Health Informatics
• Data Science
• Clinical Research

Background:

• Health information technology adoption yields extensive electronic clinical data.
• Data fragmentation across systems hinders cross-referencing due to format, vocabulary, and identifier inconsistencies.

Purpose of the Study:

• To explore strategies for overcoming data fragmentation challenges.
• To highlight the roles of various data management architectures in facilitating health data reuse for research.

Main Methods:

• Review of data consolidation and standardization techniques in healthcare.
• Discussion of data warehouses, data lakes, datamarts, and feature stores for data management.

Main Results:

• Data warehouses consolidate and standardize data for research.
• Data lakes store raw data and metadata for flexible exploration.
• Datamarts refine data for specific research questions, and feature stores optimize data for analysis.

Conclusions:

• Data warehouses are vital for health data management.
• Data lakes, datamarts, and feature stores are essential complementary tools for enabling efficient health data reuse in research and analysis.