Data Lake Vs. Data Warehouse – What are the Key Differences?

Data Lake Vs. Data Warehouse

The terms Data Lakes and Data Warehouses are two different varieties of data storage repositories, mainly used for storing Big Data. Both the repositories are highly utilized by the organizations for managing, storing and analyzing the data. A Data Lake is a vast pool that stores mountains of raw data. It can store both structured and unstructured data in its original format and able to access it as per your requirements.

Nowadays, the Data Warehouse can be widely used by enterprises for storing structured data, designed especially for specific business purposes. The users are able to make better business decisions by extracting the data from Data Warehouses. First and foremost, the organizations should learn the concepts of Data Warehouse and Data Lake, especially how and when to implement them. Let’s see some of the important key differences between these repositories.

Differences between Data Warehouse and Data Lake

1. Users: Data Scientist Vs. Business Professionals

Data Lakes are unique for users who are all familiar with unprocessed data. Normally, the unstructured and raw data can be highly accessed by the data scientist, who requires specialized tools with capabilities such as statistical analysis and predictive modeling.

Nowadays, the processed data can be widely used in spreadsheets, tables, charts and for some other purpose; it can be easily accessed by all the employees in your company. The Data Warehouse is designed especially for the operational users for storing structured data that is easy to use and understand.

2. Storage: Raw Vs. Structured

The cost is the most essential factor to be considered when comes to the storage of data. Storing of data in Data Lake requires minimized cost when compared with Data Warehouses. While storing the data in the Data Warehouse, the data engineers have to put a lot of effort into analyzing the data.

Even though it requires high cost and the latest technological tools, the information stored in the Data Warehouses is cleaned and transformed. The data stored in Data Lakes are raw, both in a structured and unstructured format, so retrieving it is a little complex. However, it is unique for machine learning which is able to access it quickly.

3. Data Capturing

All kinds of data such as structured, unstructured and semi-structured data are stored in its original format in Data Lakes. Whereas, in Data Warehouses, only structured data have to be stored that should be organized in a particular format for easy access.

4. Processing Time

In Data Lakes, the users are permitted to access the data, before it has been cleansed, transformed and structured. Besides this, the users are able to acquire data quickly than compared with a traditional Data Warehouse.

While retrieving the data from the Data Warehouse, it concentrates highly on the predefined data types. Therefore, it takes more time to make changes in the Data Warehouse.

5. Agility

The data of Data Warehouse are in a structured format, so it comes with low agility. On the other hand, the Data Lakes require some technical changes for retrieving the data with a structured format. The Big Data technologies used in the Data Lakes are completely new.

The information can be retrieved and reused with the help of developers and data scientists for configuring and reconfiguring their queries, models, and apps. In recent years, most of the organizations have turned to use the Data Warehouse, who wants to retrieve the data with pre-integrated reporting and BI.

6. Security

The Data Warehouse technology has been widely used in the past decades, whereas the Big Data concept for Data Lakes is relatively new. That’s why the capacity to provide security to the data in the Data Warehouse is much better than data in Data Lakes. In the Big Data industry, there are wide ranges of concepts that are established for offering extra security to the data.

7. Position of Schema

Normally, the position of the schema can be described after the data is successfully stored in the Data Lakes. Through this, the data can be easily captured and able to gain high agility but at the end of the process, it requires more work. In a Data Warehouse, the schema position can be defined before the data is stored. It requires work at the beginning of the process and offers high integration, security and performance.

Thus, these are all important key differences between Data Warehouses and Data Lakes. Hope, you have understood the major dissimilarities between these two repositories.


Ritesh Patil is the co-founder of Mobisoft Infotech that helps startups and enterprises in mobile technology. He loves technology, especially mobile technology. He’s an avid blogger and writes on mobile application. He works in a leading Android and iOS application development company that has developed innovative mobile applications across various fields such as Finance, Insurance, Health, Entertainment, Productivity, Social Causes, Education and many more and has bagged numerous awards for the same.

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