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Data Lakes vs. Data Warehouses: How to Differentiate?
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Content Team
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Both data lakes and data warehouses are widely used for storing big data. While their purpose might not seem much different, the terms data lake and data warehouse cannot be used interchangeably. Customer data platform (CDP) is also emerging to the surface and is making its name as the go-to solution for data management. Combined with data lakes, it can be a game changer due to its impeccable capabilities. You can learn more about it in What is CDP & How does it works, where we covered everything about CDP and how it can change the way your business operates.

Data Warehouse

Data warehouse is a combination of technologies and components that work together to use data strategically. Its primary operation is to gather data from various sources and methods and pool them together in a structured manner to generate beneficial business insights. A data warehouse is the reservoir of structured and filtered data that has already been processed for a particular purpose. Its purpose is to store a large amount of structured and filtered data that can later be used for query and analysis to generate essential leads for the company’s benefit instead of transactional processes.

Data Lake

A data lake is also used to store large volumes of data, but unlike data warehouses, data lakes can store data in structured, unstructured, and semi-structured formats. A data lake has no restrictions regarding storing data in any particular form; it can store raw data in its native format without any limits on file size. It also offers a large amount of data in quantity to businesses for better analytical performance and native integration. Data lakes, like data warehouses, can ingest data that comes from multiple sources; later, that data can be reserved in tables for dashboards and reports and made available downstream to feed other systems, including data warehouses.

What are the Key Differences Between Data Lake vs. Data Warehouse

Data Structure:

Putting it simply, the data that has not been processed yet for a specific purpose is identified as raw data. This attribute is perhaps the most significant difference between data lakes and data warehouses. Data lakes stores data in any format available, for example, structured, unstructured, and semi-structured; at the same time, data warehouses store processed and refined data.

Since data lakes store unprocessed data, they need a much larger storage capacity than data warehouses. Raw and unprocessed data is ideal for machine learning since it is malleable and can be analyzed at a more incredible speed for multiple purposes. However, this attribute of data lakes sometimes becomes its biggest hurdle since all that raw data can become a swamp of unprocessed data without any data governance and countermeasure in place.

In contrast, data warehouses are relatively manageable when it comes to the storage of data. They can end up helping you in managing your finances by using all that pricy storage more efficiently and not filling them up with useless data that you may never use.


It is often too challenging to navigate raw, unprocessed data for those unfamiliar with the process. It takes data scientists and specialized tools to manage and translate that raw and unprocessed data into something that can be used for a number of different business purposes.

Processed data is represented in charts, reports, dashboards, etc., so that most employees, if not everyone, can understand the contents it contains. Processed data from the data warehouses only demands that the user be familiar with the topic represented.


Accessibility, or rather how easy it is to use, refers to the usefulness of the data repository as a whole, not just the data within it. And since there is no structure when it comes to data lake architecture; therefore, it is easy to change and easy to access. On top of that, because the data lake has barely any limitations, any necessary change in data can be done quickly without much bother.

In contrast, data warehouses are more structured by default which means the architecture of data warehouses has one significant benefit compared to data lakes. Because the data inside it is processed and structured, it makes it easier to decipher, but this same attribute has its drawbacks. The limitations of architecture make it difficult and pricy to manipulate.

Other Use Cases of Data Lake and Data Warehouses

In Education, the use of big data is emerging on the surface and is becoming more apparent than ever. Data regarding students’ grades, specific difficulties, attendance, etc., can help failing students get back on their feet and help us predict future potential issues and resolve them before they surface. Flexible data solutions have also helped institutions facilitate billing, enhance fundraising, and more. In the case of Education, data lakes preferably offer the best data storage solution.

In finance, a data warehouse is the most preferred storage model because it can be structured to be accessed by the entire staff rather than only a data scientist. Big data has always helped financial services to take giant leaps, and data warehouses have been a big part of that success.

In Healthcare, data warehouses have been used for a long time. However, they never gained massive success because of the very nature of data warehouses, which could not meet the demands of the healthcare industry. For example, in Healthcare, the data is primarily unstructured, containing doctors’ notes, clinical data, etc. But, as mentioned throughout the blog, data warehouses are not known for storing unstructured data.

Although unstructured data is one of the primary reasons why data lakes are a better choice for the Healthcare industry, there are other reasons as well, such as the need for real-time insights and more, for which data warehouses are not an ideal model.

In Automobiles, more prominently in supply chain management, the prediction capabilities of a data lake can have huge benefits because of the flexible data stored in a data lake. The advantage is mostly cost-cutting after examining the data via forms within the transport pipeline.

How does Axeno fit into all this?

Here at Axeno, our goal is to provide professional services that we designed to facilitate the use of ever-evolving technology by enterprises and end users. We focus on improving user experiences and Digital Transformation services. Our services are designed to nurture your specific business needs and user requirements.


Management, storage, and use of data are crucial tasks for any company or service. Choosing the right method of storing large chunks of data almost entirely depends on the use case you have in mind. Data warehouses could be your best option if you want to store and use the data for a specific purpose, such as making reports, dashboards, etc. But if you’re going to store a large amount of data for later use and have all the flexibility you need to utilize it later, then data lakes might be the perfect option. The best course of action is to determine what best fits your needs and then make a decision.