Because of the absolute volume of data we produce in this era, every facet of data has become vital for a business, from processing to storing. You may have heard of “data lake” and “data warehouse” when storing large amounts of data. These are the two most frequently methods for storing large amounts of data.

I’ve been in the data sector for a long time and can attest that a data warehouse and a data lake are not the same things. Despite this, I notice a lot of people using these terms interchangeably. Data lake and data warehouse terms and their differences and applications are critical for a data engineer since only then will you be able to determine if a data lake is suitable for your company or data warehouse. Lets take a look at Data Lake vs Data Warehouse;

Data Warehouse

A data warehouse is a sum of technologies and components used to make strategic data decisions. It gathers and maintains data from a variety of sources in order to give actionable business insights. But it refers to the electronic storing of a huge volume of data for inquiry and analysis rather than transaction processing. It is the conversion of data into information.

Data Lake

A data lake is a large-scale storage container for structured, semi-structured, and unstructured data. It’s a place where you can store any type of data in its original format, with no restrictions on account size or file size. It provides a significant amount of data for improved analytical performance and native integration.

A data lake is a huge repository that looks a lot like a lake or a river. Similar to how a lake has various tributaries, a data lake has structured data, unstructured data, machine-to-machine communication, and logs flowing through it in real-time.

How data kept in a data lake vs. a data warehouse

The Data Lake stores all forms of data in their raw form, including structured and unstructured data. Data lake storage contains information that may be valuable in the now but is also likely to be useful in the future. Regardless of the source or form of the data, it is stored in the data lake. The data stored in its unprocessed state. When it’s time to use it, it’ll be altered.

The data warehouse only contains high-quality data that is pre-process and is ready for the team to use. Data extracted from transactional systems or data including quantitative measures and their attributes will be stored in a data warehouse. Cleansing and transformation of data

History Data Lake Vs. Data Warehouse

The employment of big data analytics in data lakes is still relatively new. Unlike big data, the data warehouse concept has been around for decades.

Purpose Data Lake Vs. Data Warehouse

Data Lake services provide goals that aren’t set in stone. Organizations sometimes have a future use case in mind. Data discovery, user profiling, and machine learning are some of its most common applications.

The data warehouse includes data that has been per-designed for a certain use case. Business Intelligence, Visualizations, and Batch Reporting are just a few of the applications.

Users’ Data Lake Vs. Data Warehouse

Data lakes utilized by data scientists to discover trends and relevant information that can aid enterprises.

Whereas, Data warehouses used by business analysts to develop visualizations and reports.

Pricing: Data Lake Vs. Data Warehouse

Data lake services don’t pay much attention to storing information in a structured fashion. Therefore, this is relatively low-cost storage.

Data storage is a more expensive and time-consuming operation in the data warehouse.

Cloud Data Lakes VS Data Warehouse

While both data lakes and data warehouses contribute to the same goal, data lakes complement cloud data warehouses better. According to ESG research, about 35-45 percent of organizations are actively considering the cloud for functions such as Hadoop, Spark, databases, data warehouses, and analytics applications, and this is a trend that is growing due to cloud computing’s benefits, such as massive economies of scale, reliability, and redundancy, security best practices, and easy-to-use managed services. Cloud Data Warehouses combine these advantages with traditional data warehouse functionality to improve performance and capacity while reducing maintenance costs.

Which is Best?

The differences between both a “data lake” and a “data warehouse” is here. Based on these key differences, let’s make a decision on which is best.

The data you collect will be primarily unstructured if your company deals with healthcare or social media (documents, images). The amount of structured data is really small. As a result, the data lake is an excellent fit because it manage both types.

If your web firm divided into several pillars, you’ll want to have dashboards that summarize all of them. In this scenario, data warehouses will aid in making educated decisions. It will ensure that the data is of high quality, consistent, and accurate.

The majority of the time, businesses use a combination of the two. They use the data lake for data exploration and analysis before moving the rich data to data warehouses for quick and advanced reporting.


The debate over “data lake vs. data warehouse” is likely only start, yet each model is distinct due to major differences in structure, process, users, and overall agility. Developing the correct data lake or data warehouse, depending on your company’s demands, will be crucial to its success.

Related Post: Snowflake Vs. Azure Synapse