Data lake and data warehouses are widely used for data storage. Although they serve the same purpose, the terms cannot be used interchangeably. In this article, we make a comparison between a data warehouse and a data lake to give you a clear understanding of why they are not the same.
What Is Data Lake?
A data lake is a centralized repository where you can store all your data, be it structured or unstructured. In a data lake, you can store data in its raw form without having to structure it first. In addition, you can run all types of analytics including dashboards, machine learning, and real-time analytics that can guide you to better decisions.
What Is Data Warehouse?
A data warehouse is optimized to analyze relational data sourced from business applications and transactional systems. The data structure and schema will be already defined.
Data Lake Vs Data Warehouse
Here is a comparison between a data lake and data warehouse
Data lakes store raw, unprocessed data whereas data warehouse stores processed data. This is the key difference between the data lake and the data warehouse. The raw data in the data lake is not processed for any purpose at the time of storage.
Because they store all types of data, a data lake requires larger storage space than that required by the data warehouse. Additionally, the malleability of the raw data in the data lake makes it ideal for machine learning. However, the risk associated with raw data is more because, in the absence of appropriate data governance measures and data quality, the whole data lake may turn out into a data swamp without any useful data that can be accessed with ease.
There is no fixed purpose for raw data stored in the data lake. Sometimes, raw data might be stored into a data lake by keeping future use in mind. However, the processed data in the data warehouse has well-defined purposes. No storage space is wasted in the data warehouse and no data is left unused forever.
People who are unfamiliar with unprocessed data and have little idea about using them will find it difficult to navigate through the data in the data lake. Processing of data in data lake requires a data scientist you can understand and translate the data using specific tools.
Processed data in the data warehouse is mostly used by business professionals. It merely requires the user to be familiar with the topic being presented in the form of charts, tables, and spreadsheets.
Both the data lake and data warehouse has wide-ranging business applications.