20+ Difference Between Data Lake And Data Warehouse

At least 50% of the world’s population currently qualifies as technologically impaired while living in a highly developed civilization.

Even as tech enthusiasts, we often find ourselves puzzled by the various changes that are happening, thus creating a vast knowledge gap.

Thus, to aid this gap of knowledge, in this article, we will be attempting to understand the various differences that mainly exist between the two diverse structures of a data lake and a data warehouse, respectively.

Table of Contents

Comparison Between Data Lake and Data Warehouse

Particulars	Data Lake	Data Warehouse
Data Storage	The structure in relation to a data lake is specifically comprised of an organization’s raw and unstructured data. As a component in relation to storage capacity, the structure in relation to a data lake has the ability to store data indefinitely for the purpose of immediate or future use.	The structure in relation to a data warehouse is specifically comprised of an organization’s structured, clean, and processed data, which is ready for strategic analysis based on a set of predetermined business needs.
Data Structure	The structure in relation to the idea of a data lake comprises a form of an organization’s data, which can particularly be characterized as raw data, unlike that of the structure in relation to the idea of a data warehouse.	The structure in relation to the idea of a data warehouse comprises a form of an organization’s data that can be particularly characterized as processed data, unlike that of the structure in relation to the idea of a data lake.
Purpose of Data	The structure in relation to the idea of a data lake comprises raw data that has not been particularly determined at a given point in time, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.	The structure in relation to the idea of a data warehouse comprises data that has been processed and is supposed to be in use by the organization’s managers and business-end users at a given point in time, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.
Accessibility	The data that is present in the structure in relation to the idea of a data lake is considered to be highly accessible and quick to update, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.	The data that is present in the structure in relation to the idea of a data warehouse is considered to be highly inaccessible, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.
Users	The structure in relation to the idea of a data lake is most generally used by data scientists and engineers, particularly for the purpose of studying data while it is still in its raw form in order to gain new and unique business insights.	The structure in relation to the idea of a data warehouse is most generally used by the organization’s managers and business-end users, particularly for the purpose of gaining insights from business KPIs (Key Performance Indicators).
Analysis	The structure in relation to the idea of a data lake is often referred to for the purpose of analyzing data when it comes to subjects such as predictive analytics, machine learning, data visualization, business intelligence, and big data analytics, respectively.	The structure in relation to the idea of a data warehouse is often referred to for the purpose of analyzing data when it comes to subjects such as data visualization, business intelligence, and data analytics, respectively.
Schema	The structure in relation to the idea of a data lake involves the schema being defined after it has been stored, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.	The structure in relation to the idea of a data warehouse involves the schema being defined before the data is stored, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.
Speed	This working process of the structure in relation to the idea of a data lake is considered to be comparatively more faster than that of the structure in relation to the idea of a data warehouse, particularly by way of making the process of capturing and storing such raw data in a timely manner.	The working process of the structure in relation to the idea of a data warehouse is considered to be comparatively longer and slower than that of the structure in relation to the idea of a data lake. However, this methodology of working and storing data also makes the set of data consistent across the organization.
Processing	The structure in relation to the idea of a data lake in particular reference involves a set of data being extracted from its sources for the purpose of storage, after which it is supposed to be sent for structuring. However, the set of data is only sent for the purpose of structuring when the organization particularly needs such data to be structured for its own use.	The structure in relation to the idea of a data warehouse in particular reference involves a set of data being extracted from its sources, after which it is scrubbed and consequently structured to make such data ready for business-end analysis as and when it is required by the particular organization.
Costs	The structure in relation to the idea of a data lake involves a comparatively cheaper model of cost when it comes to the process of storing data, unlike the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.	The structure in relation to the idea of a data warehouse involves a comparatively costlier model of cost when it comes to the process of storing data, unlike the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

Contrast Between Data Lake and Data Warehouse

What exactly is a data lake in relation to?

The structure in relation to a data lake refers to a highly scalable mechanism that provides an organization with the ability to store its data in its raw and original form without any kind of fixed limitation on the size of the account or any defined purpose for such a set of data.

What exactly is a data warehouse in relation to?

The structure in relation to a data warehouse refers to a large repository of organizational data that is particularly accumulated by way of a wide range of operational and external data sources.

The structure in relation to a data warehouse thus allows an organization to store its data in a structured and filtered form, which is already processed for a specifically defined purpose.

Major Differences Between Data Lake and Data Warehouse

Data Storage

Data Lake: The structure in relation to the idea of a data lake in particular reference is specifically comprised of an organization’s raw and unstructured data. In the context of this raw and unstructured data, as a component in relation to storage capacity, the structure in relation to the idea of a data lake has the ability to store data indefinitely for the purpose of immediate or future use.

Data Warehouse: The structure in relation to the idea of a data warehouse in particular reference is specifically comprised of an organization’s structured, clean, and processed data, which is ready for strategic analysis based on a set of pre-determined business needs by a team of business-end members of that particular organization.

Data Structure

Data Lake: The structure in relation to the idea of a data lake in particular reference is specifically comprised of a form of an organization’s data, which can particularly be characterized as a set of raw data, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.

Data Warehouse: The structure in relation to the idea of a data warehouse, in particular, reference is specifically comprised of a form of an organization’s data that can be particularly characterized as a set of processed data, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

Purpose of Data

Data Lake: The structure in relation to the idea of a data lake in particular reference is specifically comprised of a set of raw data that has not been particularly determined at a given point in time, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.

Data Warehouse: The structure in relation to the idea of a data warehouse, in particular, reference is specifically comprised of a set of data that has been processed and is supposed to be in use by the organization’s managers and business-end users at a given point in time, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

Accessibility

Data Lake: The data that is particularly present in the structure in relation to the idea of a data lake is considered to be highly accessible and quick to update, whereby it is comparatively cheaper to make changes, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.

Data Warehouse: The data that is particularly present in the structure in relation to the idea of a data warehouse is considered to be comparatively more complicated, wherein it is extremely costly to make changes, thus making it highly inaccessible, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

Users

Data Lake: The structure in relation to the idea of a data lake, in the most general sense, is referred to and used by persons who identify as data scientists and data engineers, particularly for the purpose of being able to study data while it is still in its raw form in order to gain new and unique business insights.

Data Warehouse: The structure in relation to the idea of a data warehouse is most generally used by the organization’s managers and business-end users, particularly for the purpose of gaining insights from business KPIs (Key Performance Indicators).

In this context, it is essential to understand that the structure in relation to the idea of a data warehouse has been structured to be able to provide answers to pre-determined questions for further analysis.

Analysis

Data Lake: The structure in relation to the idea of a data lake is often referred to, particularly by data scientists or data engineers, for the purpose of analyzing data when it comes to subjects such as predictive analytics, machine learning, data visualization, business intelligence, and big data analytics, respectively.

Data Warehouse: The structure in relation to the idea of a data warehouse is often referred to, particularly by business-end users or managers of the business, for the purpose of analyzing data when it comes to subjects such as data visualization, business intelligence, and data analytics, respectively.

Schema

Data Lake: The structure in relation to the idea of a data lake, in particular, involves the schema being defined after it has been stored in the capacity, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.

Data Warehouse: The structure in relation to the idea of a data warehouse, in particular, involves the schema being defined before the data is stored in the capacity, unlike that of the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

Speed

Data Lake: In particular reference to the capacity of storing data in the structure in relation to the idea of a data lake, the related working process, in this context, is considered to be comparatively faster than that of the structure in relation to the idea of a data warehouse, particularly by making the process of capturing and storing such raw data in a timely manner.

Data Warehouse: In particular reference to the capacity of storing data in the structure in relation to the idea of a data warehouse, the related working process, in this context, is considered to be comparatively longer than that of the structure in relation to the idea of a data lake.

However, this methodology of working and storing data also makes the set of data consistent across the organization.

Processing

Data Lake: The structure in relation to the idea of a data lake in particular reference involves the process of a set of data being extracted from its sources for the purpose of storage, after which it is supposed to be sent for structuring.

However, it is of important nature to understand in the present context that such a set of data is only sent for the purpose of structuring when the organization particularly needs such data to be structured for its own use.

Data Warehouse: The structure in relation to the idea of a data warehouse, in a particular reference, involves the process of a set of data being extracted from its sources, after which it is scrubbed and consequently structured for the purpose of making such a set of data ready and presentable for business-end analysis as and when the particular organization requires it.

Costs

Data Lake: The structure in relation to the idea of a data lake involves a comparatively cheaper cost model when it comes to storing data, unlike the situation that is particularly posed in the context of the structure in relation to the idea of a data warehouse.

This is also because since data lakes are considered less time-consuming and more manageable, they bear exceptionally less operational costs compared to the structure in relation to the idea of a data warehouse.

Data Warehouse: The structure in relation to the idea of a data warehouse involves a comparatively costlier model of cost when it comes to the process of storing data, unlike the situation that is particularly posed in the context of the structure in relation to the idea of a data lake.

This is also because since data warehouses are considered to be more time-consuming and difficult to manage, they bear exceptionally high operational costs compared to the structure in relation to the idea of a data lake.

Frequently Asked Questions (FAQs)

What are the benefits associated with the structure in relation to the idea of a data lake?

Organizations have often placed their preference on the structure in relation to the idea of a data lake for the purpose of storing data over that of the structure in relation to the idea of a data warehouse since such a set of data can be stored in a cost-effective manner, readily available for faster use, and has a wider scope of being able to derive a broader range of previously unavailable insights.

What are the benefits associated with the structure in relation to the idea of a data warehouse?

Organizations have often placed their preference on the structure in relation to the idea of a data warehouse to store data over that of the structure in relation to the idea of a data lake since such a set of data is readily available for use without any data prep, which further makes it more accessible and easier to use for the business-end users of that organization.

Moreover, such a set of data is considered accurate, complete, and unified, making the process of gaining better business insights easier than before.