What is a Data Lake?
A data lake is a central storage repository used to store a large amount of raw, granular data in its native format. It is a single store repository containing structured data, semi-structured data, and unstructured data.
A data lake is used where there is no fixed storage, no file type limitations, and emphasis is on flexible format storage for future use. Data lake architecture is flat and uses metadata tags and identifiers for quicker data retrieval in a data lake.
Data in a data lake is not filtered before storage, and accessing the data for analysis is ad hoc and varied. The data is not transformed until it is needed for analysis. However, data lakes need regular maintenance and some form of governance to ensure data usability and accessibility. If data lakes are not maintained well and become inaccessible, they are referred to as “data swamps.”
Data Lakes vs. Data Warehouse
Data lakes are sometimes confused with data warehouses; Therefore it is important understand data lakes, it is crucial to acknowledge the fundamental distinctions between the two data repositories.
As indicated above, both are data repositories that serve the same overall purpose and objective of storing organizational data to support decision-making and analytics. Data lakes and data warehouses are alternatives and mainly differ in their architecture, which can be concisely broken down by the following areas: Data Structure, Flexibility, and User Interface.
The schema for a data lake is not predetermined or structured before data is applied to it, which means data is stored in its native or original format containing structured and unstructured data. Data is processed when it is being used. However, a data warehouse schema is predefined and predetermined before the application of data, a state known as schema on write. Data lakes are termed schema on read.
Data lakes are flexible and adaptable to changes in use and circumstances, while data warehouses take considerable time defining their schema, which cannot be modified hastily to changing requirements. Data lakes storage is easily expanded through the scaling of its servers.
Accessibility of data in a data lake requires some skill to understand its data relationships due to its undefined schema. In comparison, data in a data warehouse is easily accessible due to its structured, defined schema. Many users can easily access warehouse data, while not all users in an organization can comprehend data lake accessibility.