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Data warehouse vs Data Lake: How do they differ?

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data lake vs data warehouse

Big data is stored in both warehouses and lakes. However, you need to remember that these are not interchangeable terms.

The data warehouse contributes to being the repository, which helps store the filtered and structured data, which is processed for a certain objective.

Though both kinds of data storage are confused often, they are known to be quite different.

Data warehouse and lake differ at the high-level objective in the aspects of storing datasets.

They both serve various objectives and hence the difference is vital. They need various sets of eyes for the proper optimization.

Though the data lake might work properly for a business enterprise, the data warehouse is an appropriate fit for the other. You can explore the major differences between data warehouse and lake by scrolling through the article.

Retaining the data

A lot of time is consumed in data analytics from different sources while getting into the data warehouse development process.

Data analytics also aids in understanding the business procedures and profiling of datasets. The report generated from data analytics would be structured that can be utilized for further processing.

Data analytics may seem to be a time-consuming process but the information must be scrutinized and then included in the warehouse to avoid breaches. If there is junk data in the analytics phase, it can be excluded from warehousing the data. This simplifies the data structures and excessive space occupied by insignificant data in disk storage.

The data lake, on the other hand, is responsible for retaining all types of data. You can use the data lake for the data used today and the data used in the past. Such a type of approach is possible as the data lake hardware differs greatly from the one used for the data warehouse.

Data lakes offer support to different users.

The vast community of users is operational in major businesses.  These users grab the data analytics reports, analyze the performance, and then cut out similar datasets by manipulating them with spreadsheets.

The data warehouse is considered a suitable choice for potential users as they are user-friendly, well structured, and easy to understand.

Data analytics serves as the other phase, which makes data warehousing a primary source. Despite the primary source, they revert back to data sources to find appropriate information stored in warehouses.

The spreadsheets are utilized as the desired tool for data manipulation as it eases the job of generating new reports that can be shared across enterprises. Data warehouse utilization goes beyond bounds in this era and is the go-to source of data for businesses.

Users can get proper support from the data lake approach. Data scientists prefer data lakes to work in the appropriate manner with large and distinct datasets. The other data scientists make the right use of the structured data view for the use.

Data lakes offer support to all data types.

A data warehouse comprises the data which is extracted from different transactional systems. It comprises the quantitative metrics and the attributes, describing them.

In this aspect, non-traditional sources of data like images, text, social network activity, sensor data, and web server logs are ignored largely. The data lake approach is known to embrace such types of non-traditional data types.

 Within the data lake, all the data are kept, irrespective of the structure and source. Here, the data is kept in the raw form, after which it is transformed once it is ready for use. Such an approach, used in the data warehouse, is referred to as “Schema on Read” vs the “Schema on Write”.

Data lakes is adapting to changes easily.

The data warehouse has the major complaint of consuming time to change datasets and it takes ample time for development.

A proper data warehouse architecture can help in being compatible with the changes owing to data loading complexities, report generation, and analysis. These kinds of changes in data warehousing require huge developer resources.

Instead, in data lakes, the information is recorded in raw form which would be easier for people to access. This helps the community audience to dig deeper than warehouse structure and manipulate datasets.


Data lakes are known to be bigger in size, as they retain the data, which is relevant to the company. On the other hand, data lakes are petabytes in size. According to research, data warehouses are known to be more selective, according to the data which is stored.


Data lakes are utilized for storing incoming data by data engineers. The data lakes’ use cases are beyond the storage of information. These unstructured datasets are scalable, flexible, and ideal for big data analytics. Hadoop & Apache spark aid in big data analytics with these unrefined data.


The data warehouses can be used for read-only purposes for different analysts and users who are collecting and investigating the data sets to grab insights. As the data is archival and clean, you do not need to carry the hassles of updating and inserting the data.

The data lake is an excellent choice for potential users who want to ensure an in-depth analysis.

Such types of users include different data scientists who require different advanced analytical tools and capabilities, like statistical analysis and predictive modeling. On the other hand, the data warehouse is believed to be the best choice for the operational users owing to the ease of use and well structure.

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