Data Lakehouses in Healthcare and what are it's Benefits

Data Lakes

In recent years, data lakes have emerged as a viable alternative to traditional data warehouse systems. Data lakes avoid the requirements of rigid schema-based storage models and can be used to store structured, semi-structured, or unstructured data. What’s more, data lakes can scale more easily than traditional data warehouses. Data lakes are particularly useful for managing data from many different sources and in many different formats, which face a variety of scalability, governance, and security requirements.

However, the lack of a schema-based architecture leads to a different challenge: the data lake quickly becomes a data swamp due to the growing collection of assorted data assets. Metadata management becomes challenging as data types increase in variety and complexity. For modern machine learning algorithms, especially with the critical nature of healthcare analytics, it’s crucial that these large data storage systems have an efficient mechanism for data management and querying across multiple data sources. 

Deep Dive in Data Lakehouses 

So, neither traditional data warehouses nor data lakes are ideal for healthcare data analytics projects on their own. However, a more recent type of data platform – the data lakehouse – may fit the bill. Data lakehouses combine the benefits of data warehouses and data lakes while overcoming their challenges.

Data lakehouses are low-cost, scalable open-format storage systems that allow users to store data in various formats and structures while also applying the necessary data management processing capabilities. Data lakehouses allow for faster processing than traditional data warehouses, which is critical in time-sensitive biomedical analytics. The resulting data is also more reliable, as the data lakehouse architecture features a consistent data management and governance framework. And, unlike traditional data warehouses, data lakehouses can process large data assets and accommodate new metadata management features.

Data lakehouses also support the varied formatting requirements and specifications of modern analytics and machine learning services. The data becomes available faster and more consistently despite the complex metadata features. So healthcare organizations derive real-time and proactive insights from the complex, heterogenous, and rapidly exploding volumes they experience. 

In summary, data lakehouses can help healthcare organizations improve their data quality, create real-time data access, develop more useful analytics, and scale their systems as data needs increase or change. What’s more, they also benefit from cost savings. By consolidating data from multiple sources into a single system, data lakehouses can help healthcare organizations reduce storage costs and minimize the need for complex data integration systems. Data lakehouses can help healthcare organizations make better use of their data, leading to improved patient care, more efficient operations, and better outcomes.

Compliance and Data Sensitivity

One of the biggest issues regarding data management in health care is always going to be privacy, particularly in light of such regulations as HIPAA. Beyond simply improving data management efficiency with a scalable data lakehouse system, any data platform you use should also guard access based on fine-grained and complex access control policies. 

Settings that govern who can access data are called Identity and Access Management (IAM) controls, and these come in several different categories. The most traditional is Role-Based Access Control (RBAC). RBAC means only people with certain predefined roles can see the data. For example, you can limit access to a particular set of data to anyone with an admin role, and the system can automatically shut out any other users who do not fulfil the requirements of that role. There are some downsides to RBAC, however. The most notable is that a significant percentage of data leakage comes from misused or leaked permissions and passwords, so RBAC is not the most secure option. 

Another method is Policy-Based Access Control (PBAC). PBAC bases access permissions on predefined factors like user attributes, the attributes of the data itself, or even the time of day. PBAC overlaps with Attribute-Based Access Control (ABAC), which bases access permissions specifically on the attributes of the data. PBAC allows a system administrator to fine-tune exactly who has access to specific datasets and when. 

For example, unlike RBAC, PBAC could allow the system to shut out any user who tries to access the data outside of a specific timeframe and also alert relevant personnel. This reduces the chance of a third party successfully using stolen credentials to access the datasets. As such, PBAC is the recommended method of IAM for advanced data protection and security in data lakehouses. 

Ultimately, implementing an effective access control model is critical to data security in every industry, especially when it comes to protecting the privacy and security of patient data in health care.

Looking to leverage the benefits of Data Lakehouses in the healthcare industry? Learn how Mactores can help you!