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An Introduction to Data Lakes

In recent years, there has been a growing number of organizations that have adopted the data lake structure in managing their growing data. But what makes data lakes a viable data storage solution? We shall be taking a deep dive into the data lakes in this article.


Much like the data warehouse, the data lake is a centralized repository that stores raw and unprocessed data from different sources. However, data lakes provide greater flexibility for data analysis and processing as it allows data to just be stored in its original format, without the need to structure and organize the data which is usually required in the data warehouse.

As such, companies that do not yet need nor have the means for a more complex data storage solution but are anticipating they might need such in the future will find the data lake an affordable solution that is scalable according to their present and future data needs. The data lake also provides considerable savings in both time and money as a “less frills” data management and storage solution, allowing them to easily process and manage their data at any time.

The principles of the data lake

A data lake follows three fundamental principles that shape its architecture and functionality. These are:

  1. Schema on Read - Schema on read is the principle in which data is not validated or structured during the write process and instead lets the reader verify the data’s structure. The advantage of this approach is that it allows for easy and flexible data ingestion, encouraging applications and users to write any data to the data lake. However, it’s important to note that without proper governance, the data lake can become cluttered and challenging to navigate.

  2. In-place analytics – Another characteristic of schema on read is that it allows data to be read in various ways from the same data file instead of having to move the data to different tables as is the practice for traditional analytics. This also eliminates the need for making multiple copies of the dataset, resulting in significant storage cost savings and reduced data duplication.

  3. ELT – Data lakes employ an approach known as ELT. (Extract, Load, Transform, Load) This means that in the data lake, the data is first extracted and loaded, and then transformed based on specific user requirements and use cases. This order allows for the flexibility of schema on read.

Managing data in the data lake

Because a data lake is just an accumulation of data waiting to be used, there is a risk of disorganization with the data stored there. This leads to the data lake becoming a “data swamp” leading to the deterioration of the data quality, as well as the data’s usefulness and value to the organization, deteriorate. At some point, the data in the data swamp becomes dark data, which data an organization owns but is unable to find, identify, optimize, or use.

That being said, data lakes require support, often by professionals with expertise in data science, to maintain them and make the data useful.


Data lakes have revolutionized data storage and analytics thanks to their limitless scalability, schema on read, and in-place analytics. They are a game-changer for data-driven organizations, enabling them to leverage data, regardless if it is structured, semi-structured, and unstructured, in a unified and flexible environment. The principles and technologies behind data lakes open up a world of possibilities for data-driven insights and innovation.


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