One of the goals of Skin Cancer Audit and Research Data Inc aims to establish data lakes of deidentified skin cancer results and processed data in databases
A database and a data lake are both data storage systems, but they have different architectures and use cases.
A database is a structured data storage system that is designed for transactional processing and online transaction processing (OLTP). Databases are typically organized into tables with well-defined schemas, and they support ACID (Atomicity, Consistency, Isolation, Durability) properties to ensure data integrity and consistency. Databases are optimized for handling a large number of small, structured transactions in real-time, such as processing financial transactions or managing customer data.
On the other hand, a data lake is an unstructured or semi-structured data storage system that is designed for batch processing and analytical processing (OLAP). Data lakes store raw, unprocessed data in its native formats, such as log files, images, videos, and other unstructured data sources. Data lakes allow organizations to store and process large amounts of data at scale and perform analytics and data exploration on the data. Data lakes often use distributed storage systems optimized for handling large, complex queries and analytics workloads.
Security is also a factor between the different models, as databases and data lakes differ in how they approach security.
Databases often rely on a structured approach to security, with a defined set of rules and permissions for accessing and manipulating data. This typically involves using access controls such as user authentication, role-based access control, and data encryption to restrict access to sensitive data. Databases also typically have a well-defined schema that enforces data consistency and enables the use of data validation techniques to ensure data quality. This approach can be practical for managing structured data in a controlled environment, but it may not be as flexible or scalable as data lake security.
Data lakes, on the other hand, often take a more flexible approach to security. Data lakes are designed to store various data types, including structured and unstructured data, making enforcing a consistent set of rules and permissions challenging. As a result, data lake security often relies on a combination of technologies and techniques, including encryption, access controls, and data governance policies, to ensure the security and privacy of data. Data lake security also often involves monitoring and auditing data usage, with the ability to trace data lineage and identify potential security breaches. This flexible approach to security can be beneficial for managing diverse data sets and enabling rapid data exploration and analysis. Still, it may require more advanced security expertise to implement effectively.
While databases are optimized for handling structured data in real-time, data lakes are optimized for storing and processing large volumes of unstructured data for analytical purposes. Both have unique advantages and use cases, and many organizations use a combination of both to manage their data. This is why we believe Skin Cancer Audit and Research Data Inc should aim to provide both options to researchers.