Data Lake vs Data Warehouse: Understanding the key differences

Data lake and data warehouse both are technologies that are related to data. Both have different purpose and usage. In this article, we are going to talk in detail what data lake and data warehouse is and how they are different from one another. 

Understanding the Key Differences:

Here we shall talk individually about Data Lake and data warehouse and then point out the key differences between them.

What is Data Lake?

You can call Data Lake a storage repository that is used for storing a large amount of both structured and unstructured data. With that, it also has the capacity to store semi-structured data. In short, Data Lake is considered to be a place that can store every type of data in its native format. It has no fixed limits on account size or even file. When we discuss about analytics, there are certain tools like tableau that are good for visualization and analytics. For increased analytical performance and native integration, it has a large amount of data quantity to offer.

We call Data Lake a very large container that is also similar to a real lake or river. It is same as a lake, where you have got multiple tributaries coming your way. This includes structured data, unstructured data, and logs flowing through real-time. And machine to machine.

What is Data Warehouse?

Now let us take a look at what Data Warehouse is.

It is known to be a blend of technologies and components that is used for the strategic use of data. Data Warehouse collects and manages different data from varied sources and then provides meaningful business insights. Data Warehouse is an electronic storage house of a large amount of information that has been designed for query and analysis instead of transaction processing. In short, it is a very efficient process of transforming data into information. 

Purpose of Data Lake:

The main purpose of Data Lake is to store data. It is a large size storage repository that is used for holding large amount of raw data and that too in its original format. 

With Data Lake, every data element gets a unique identifier and is then tagged with a set of extended metadata tags. Similarly, it offers a wide range of analytic capabilities. 

Purpose of Data Warehouse:

The purpose of Data Warehouse on the other hand is to store data in files and folder which can then help to organize and use the data for making different strategic decisions. With Data Warehouse, you get a multi-dimensional view of atomic and summary data. With Data Warehouse, you can perform the following functions.

  • Extracting the data.
  • Cleaning the data.
  • Transforming the data.
  • And lastly, loading and refreshing the data. 

Key Difference in Terms of Storage:

Data Lake:

On the basis of storage, Data Lake keeps all the data irrespective of the source and its structure. But know that, all this data is kept in raw form and is only transformed when ready to e used. 

Data Warehouse:

Whereas, in Data Warehouse only the data that has been extracted from transactional systems will be stored. This data will consist of quantitative metrics along with their attributes. Note that, this data is cleaned and transformed. 

Differences in Terms of Users:

Data Lake:

In terms of users, Data Lake is suitable for all those people out there who wish to indulge in deep analysis. Such type of users can be data scientists specially who need advanced analytical tools and require capabilities like predictive modeling and statistical analysis. 

Data Warehouse:

If we talk about Data Warehouse then it is suitable for all the operational users as it is quite easy to use, well structured, and easy to understand.

Difference in Terms of Data Timeline:

Data Lake:

In terms of data timelines, Data Lake has the capacity to retain all the data. By that, we don’t just mean the data that is in use but the one that might be used in the future. With that, Data Lake keeps the data for all time so as to go back in time and do an analysis. You can check out more examples of data lake as reference.

Data Warehouse:

Contrary, in Data Warehouse development process, it can take a lot of time in analyzing the various data sources. This can be a little inconvenient for some people. 

Key benefits of Data Lake:

Data Lake comes with a variety of benefits, some of them are mentioned below.

  • It offers unlimited scalability.
  • With that, it offers excellent integration with IoT. 
  • Similarly, it offers quite the flexibility.
  • In addition, the data that is collected from diverse sources is stored in the raw format.


Key benefits of Data Warehouse:

Data Warehouse also has a variety of benefits to offer which are mentioned below.

  • Data Warehouse enhances conformity and quality of data.
  • It enables historical insight.
  • Similarly, it boosts efficiency.
  • With that, it offers data security and scalability.
  • Moreover, it increases the power and speed of data analytics. 

Point-to-Point Key Differences:

Below are some point-to-point differences between Data Lake and Data Warehouse.

  • On one side, Data Lake is used to store data of all types irrespective of the source and structure. Data Warehouse on the other side lets you store data in quantitative metrics with their attributes.
  • In Data Lake, you get to define the schema after data is stored. But Data Warehouse defines the schema before you store the data.
  • Similarly, Data Lake used the Extract Load Transform process. And Data Warehouse uses the Extract Transform Load process.


When comparing Data Lake and Data Warehouse, we can say that Data Lake is suitable for all those who wish to do in-depth analysis. And Data Warehouse on the other hand is suitable for all the operational users. But either way, both offers different features which are equally competitive. So take a look at this article and understand the key differences between Data Lake and Data Warehouse and never stop learning. 

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