As healthcare leaders evaluate data management options to meet their growing clinical, financial, and operational demands, it is essential to distinguish between data lakes, data lakehouses, and traditional data warehouses. This article aims to clarify these concepts, discover their differences, and understand why modern healthcare data needs an effective solution.
As healthcare leaders evaluate data management solutions for their increasingly complex clinical, operational, and financial needs, there is increasing confusion about data lakes versus data warehouses. While they do serve overlapping purposes, they possess distinct functions and characteristics.
Data lakes, for instance, store all types of raw and unstructured data, allowing data scientists to use it for various projects. They prioritize storing data from diverse sources without considering the use of the data. Conversely, data warehouses store cleaned and processed data, making it suitable for healthcare analytics or operational reporting and specific business intelligence, quality, and financial outcomes use cases.
There is also an emerging concept worth knowing: Data lakehouse, which offers desired flexibility and scalability by combining the best aspects of a data lake and a data warehouse.
Understanding the relationship between data management systems is fundamental to investing in the right technology and meeting the various use cases in healthcare.
This article explores the differences and areas of overlap between data lakes, data warehouses, and data lakehouses.
The evolving healthcare data environment created data lakes for broad data access and usability across the enterprise. These lakes have symbiotic relationships with an enterprise data warehouse and a data operating system, as data can move into various zones for experimentation, research, or customization into shared data marts.
A data lake is beneficial in several ways:
Despite the numerous advantages data lakes offer, they come with significant challenges. Chief among these is the difficulty in assessing data quality or tracing the lineage of findings made by other analysts or users who previously extracted value from the same data. Additionally, implementing proper governance strategies is crucial to maintaining data confidence in a data lake.
Compared to a data lake, a data warehouse represents a single source of truth, providing a structured, processed data repository optimized for specific analytical and operational reporting needs.
Data warehouses are known for:
A data warehouse quickly ingests data and centralizes data governance, starkly contrasting with a data lake. However, modern data lakes are increasingly capable of rapid data ingestion and robust governance, particularly with cloud-native technologies.
Advancements in data warehouses democratize analytics and insights. Furthermore, improved approaches can:
That said, the cost efficiency of data warehouses versus data lakes can vary widely depending on specific use cases and implementation strategies.
A data lake and a data warehouse differ in how they store, manage, and use data. Unlike data warehouses, which deliver clean, structured data for business intelligence analytics, data lakes permanently store data of any type and format. Given the unvalidated nature of the data in data lakes, many organizations utilize data lakes for data science and machine learning purposes and use data warehouses for traditional business reporting, data analysis, and business intelligence.
However, a data lakehouse is a hybrid of a data lake and data warehouse, combining elements from both solutions. It provides a unified platform for structured and unstructured data and is designed to support a wider range of data analytics and data science use cases. While promising, implementing a data lakehouse can still pose significant challenges in terms of complexity and management.
Health Catalyst IgniteTM is built to operate as a data lakehouse, combining the best features of a data lake and a data warehouse. It enables health systems to accomplish the above objectives by streamlining data refinement processes. This allows organizations to focus on what truly matters: delivering high-quality patient care. It is designed to support a wide range of healthcare use cases and ensure that data is always prepared for analysis and reporting.
Ignite breaks down barriers to data-informed healthcare improvement, such as costs, time, access, and expertise. This solution gets the right data to decision-makers at the right time to drive massive, measurable improvements. Furthermore, Ignite’s machine learning and predictive capabilities capitalize on opportunities to incorporate advanced technology into everyday healthcare data management.
That said, modular data and analytics solutions must move beyond technological specifications and offer customization to achieve the following goals:
Undoubtedly, volumes of data will continue to flow into healthcare systems, so organizing and interpreting healthcare information is critical to today’s data-driven demands of executives, analysts, and clinicians. Data analytics architecture must evolve to accommodate exponential data growth – and drive value-based care and other quality improvement initiatives.
Therefore, distinguishing between data warehouses, data lakes, and data lakehouses is crucial for healthcare systems dealing with vast amounts of diverse healthcare data. Data lakes offer flexibility and scalability for storing unstructured data, while data warehouses provide structured and organized data for analysis.
Organizations and professionals who depend on reliable data can optimize their data management processes, improve patient care outcomes, and drive innovation by understanding and leveraging the strengths of both a data lake and data warehouse — combining the best of both solutions in a data lakehouse.
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