Data lakes and data warehouses are data storage and management systems, serving slightly different purposes for your business. Despite their differences, both serve vital roles in ensuring the success and efficiency of your organization.

The question here is how do they benefit us, and how do we tap into those benefits?

In this post, we will discuss what data lakes and data warehouses are, their similarities, and the differences that make each one unique.

Without further ado, let's dive right in.

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What is a data lake?

A data lake is a storage system or repository of raw information — usually, object blobs or files. Data lakes serve as a tool for data scientists to help improve efficiency and performance within businesses.

The primary users of data lakes are business analysts, data engineers, data scientists, product managers, executives, etc. They increase the accessibility of organizational data from different sources to end-users to leverage insights to improve business performance and cost-effectiveness.

What is a data warehouse?

A data warehouse is a storage system or repository of processed data; it is formatted and shaped for users to access for business purposes. They can be used for better understanding past performance and using that to inform decision-making and improve business performance moving forward.

This data is a collection of information from multiple sources. It is then transformed into a manageable form for businesses to use as a means to create dashboards, reports, and more. They work very well for analyzing data and for informing business decisions.

Data Lake vs. Data Warehouse

Now that we have identified what each is, let's compare them. First, let's look at the similarities and differences between data lakes and data warehouses.


Data Warehouse Data Lake Similarities

The most prominent similarity between data lakes and data warehouses is that they both refer to a data storage system used in the big data industry. Beyond that, both are used by large organizations for research and analytic purposes.

Truthfully, that is about the full list of similarities, as the two are quite different. For example, they are both used for big data but with different organizations within big data. So next, let's look at the top five defining differences between the two concepts.

Top 5 Data Lake and Data Warehouse Differences

There are several differences between a data lake and a data warehouse. Let's look at the top five differences below.

Data Lakes vs Data Warehouses

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1. Data Structure

Data lakes store data in its unprocessed raw form, allowing faster loading and accessibility by removing the transform process. In contrast, data warehouses store data in its processed form, requiring specific data structures in order to access them.

2. Purpose

Each has a different purpose and is formatted differently to suit the needs of each user audience. For example, the raw data of a lake is unfiltered and therefore can be used for many purposes, while data warehouses provide filtered data.

3. Users

Data lakes are best for data scientists and specialists as their needs are more suited for raw data. On the other hand, data warehouses are better for business professionals. They will need to tap into datasets that serve a fixed purpose, easily accomplished with the structure of a data warehouse.

4. Accessibility

Data lakes do not have a specific structure, and in that way, they are extremely easy to access and manipulate; this makes the data in a data lake more accessible than that of a data warehouse. By contrast, the data in a data warehouse has much more structure since it is for fixed and predetermined purposes.

5. Control, Flexibility, and Speed

These differences stem directly from the previous four points as they all have a compounding effect. The raw unstructured nature of data lakes makes them better for speed, flexibility, and accessibility. However, the structured nature of data warehouses makes them better for rigid control of data and representation. In addition, with data warehouses, any data changes need to be implemented directly by the development teams.

Data Lake Example

Cloud platforms make the best hosts for data lakes due to their scalability and modular services. In addition, cloud storage services like Amazon S3 have abstracted, durable, flexible, and data-agnostic architectures, making them a great choice for data lakes.

The following image makes for a great example of how a data lake works.

Data Lake ExampleImage Source

Data Warehouse Example

Data warehouses include product information, sales data, customer and supplier details, and more from multiple sources. By unifying this wide array of data from multiple diverse sources with content of varying types, a data warehouse supports better data analysis processes and results.

The following image helps illuminate the way a data warehouse operates.

Data Warehouse Example

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Data Lakes vs. Data Warehouses: Final Thoughts

By this point, you should understand the differences between the two types of data storage methods, what they do, and who typically uses them. However, for the sake of clarity, let's highlight some of the primary differences.

  • Data lakes use raw data and offer unfiltered unstructured data for big data analytics and research purposes. Data warehouses offer structured data for businesses to better inform decisions for their needs.
  • Data lakes are used much more flexibly and offer a range of data to be leveraged in any way needed. Data warehouses are less flexible, offer more stringent rules and structure, and better understand specific data uses related to the business professionals using them.
  • Data lakes are used by data scientists and specialists for a wide range of l purposes, while business professionals use data lakes for more specific needs.

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Originally published Feb 11, 2022 7:00:00 AM, updated February 11 2022