Companies create a data lake because it is scalable and secure. It can help lower the total cost of ownership of data and simplify data management. However, if it is not managed properly and regularly cleaned, it can become what’s often called a “data swamp.”
Elements of a Data Lake
To support the effective use of a data lake, it provides several essential elements:
- A searchable data catalog of all data stored in the lake
- Data governance, including a classification taxonomy that helps identify sensitive data and tools for data masking and encryption, if necessary.
- Data security features that monitor usage and only allow authorized users to access data
Read More: How Data Masking Keeps Customer Data Private, Safe And Secure
Top Uses for Data Lakes
Data lakes are used by data scientists and business analysts for a variety of big data processing and analytics. For example, business analysts can create dashboards and visualizations that help identify issues or opportunities. Data scientists can perform data mining, machine learning, and predictive analytics to identify business trends, detect fraud, or perform risk management.
Data Lake vs. Data Warehouse
A data lake is not the same as a data warehouse. Although both store diverse data from across the organization, a data warehouse stores preliminary relational and transactional data from line of business systems. In addition, a data lake stores data in its raw, natural format, whereas a data warehouse stores data that is processed, cleaned, and optimized for analysis.
Types of Data Lakes
Data lakes can exist on-premises or in the cloud. Examples include Google Storage Cloud, Amazon S3, Apache Hadopp, and Microsoft Azure Data Lake.