Data warehouse and data lake are two solutions used to store large amounts of data from multiple sources. But one cannot use the terms interchangeably.
A data warehouse stores highly structured data that is used to support specific business analytical needs. Business users can readily use the data to gain insights and make decisions about their line of business. Google Big Query is an example of a data warehouse.
A data lake extracts data from different sources and stores it as is. When the need arises, the data is transformed for use. Data engineers and scientists can leverage such data for applications like machine learning.
Azure Data Lake is a data storage solution offered by Microsoft. If you need help with any of Microsoft’s software and tools, you can consult with a third-party IT helpdesk companies.
If you are confused which data storage solution will work best for your organization, the article will present you with five differences that can help you decide.
5 Key Differences Between Data Warehouses and Data Lakes
Let us look at some integral differences between data warehouses and data lakes.
1. Type of Data Stored
Let us look at some integral differences between data warehouses and data lakes.
2. Data Processing
Let us look at some integral differences between data warehouses and data lakes.
3. Ease of Use
Let us look at some integral differences between data warehouses and data lakes.
4. Users
Let us look at some integral differences between data warehouses and data lakes.
5. Cost
Let us look at some integral differences between data warehouses and data lakes.
How to Choose the Right Data Storage Solution for Your Organization?
According to Statista, at a global level, data creation is expected to reach over 180 zettabytes by 2025. If you want to leverage data, understand what storage solution would work for your organization.
If your end use of data is predefined, for example, you want to study customer journeys based on quantitative data; then a data warehouse is a better option.
Consider a data lake if you have a large data set and are storing data for applications like machine learning.
Budget, processing speed, and ease of use are other factors you should consider.
Also, know that these solutions are not mutually exclusive. For example, you can leverage the data lake’s storage capability and combine it with data warehouse features like indexing and querying.
Summary
To summarize the differences, a data warehouse stores structured data, the schemas are predefined, and it follows the ETL processing method. Business-end users can use warehouses for applications like BI reporting.
A data lake stores unstructured data, the schema is defined post the data is loaded, and it follows the ELT processing method. Experienced data scientists can use data lakes for applications like machine learning.
If you need help with data storage support solutions, consult with an MSP to know more.