Data Lake, Data Warehouse


By HiFX Engg. Team | May 17, 2021 | 5 min read

Are the two complementing each other or is one found as a substitute for another?

The usage of the data collected increases as the type and volume of data accumulated from all enterprises inflate.

However, majority of the collected data are a possible set of interactions among the systems and stakeholders, out of which very few have been identified by the enterprises as they have not had a chance to experiment the data.

Hence, the concept of data lake has been chosen to concentrate in such a scenario. This blog emphasizes on the comparison between data lake and data warehouse, and analyze whether the two are complementary or the former replaces the latter.

The fundamental factors

Data Structure
Data Structure: Data Lake vs Data Warehouse

Considering the collection, storage, systematic labeling, categorization, and organization of data, data warehouse is likened to that of a real warehouse. Furthermore, prior to storage into the data warehouse, initially the enterprise data is processed and then converted to the required format. Notably, data arrives from a specific number of sources that delegates a set of applications.

In contrast, a huge repository of raw and unprocessed data is referred to as a data lake. The semi-structured or unstructured data can be leveraged by any prevalent business applications as well as by an enterprise for upcoming applications. In addition, data lakes can accommodate a large volume and mixture of data at a lower cost as compared to data warehouses, as they do not need a defined plan before collecting data.

Intention of the collected data
Intention of the collected data : Data Lake vs Date Ware House

The predefined functionality of data warehouse necessitates the need for structured data. Due to the high-priced method of cleaning and processing of data, data warehouses aim to be efficient with adequate storage space. Every single data is intended to be in conformity with the proposed output to the necessitated business application, thus guarantees space optimization.

However, the aim of the data being collected in a data lake is not preset, as data lake is only intended to collect and accommodate the data. In to the bargain, the venue and method of usage is decided in a later phase. Significant conclusions are derived based on the exploration and experimentation of the data based on the requirements that develop along with the innovations in an enterprise.


Compared to data warehouses, data lakes are well accessible and changeable due to its storage as a raw format.

Then again, data stacked in a data warehouse requires more time and exertion to be altered into another format. Moreover, data manipulation is also over-priced.

Will Data Warehouses be substituted by Data Lakes?

The answer is a No. Rather replacing, data lakes and data warehouses will be used in complementary terms.

The systematic and structured quality of data warehouses ensures seamless solutions to inevitable enquiries, i.e., it is built in such a way that swift real-time answers are provided. Additionally, data warehouse is considered to be the optimal choice in cases where the stakeholders would need either need a piece of data, or a set of data or metrics for regular analysis. For instance, region-wise sales, revenue, steady rise in sales, business performance curves, etc. are all dealt by the data warehouse.

However, data lakes become a requisite when various types of data begin to flow in, and enterprises strive to utilize the most of it. Notably, if and only if there is a specific purpose assigned to the ingested data, schemas are applied to the data after it is integrated along with the data lake. Furthermore, the determination of the selection of schema is based on the created metadata along with the raw data, and fitting of the data into the use case. This also implies that the uploaded data can be viewed in different structured formats, and can be used across various business applications for several purposes. Remarkably, the flexibility feature of data lakes assures data scientists to perform seamless experimentation of data, setting up of quick models, and identification of patterns to anticipate possible business opportunities.

What yet ensures the practicability of Data Warehouse?

What yet ensures the practicability of Data Warehouse?
  • Actual exploration of the collected and stored data beyond the structured capabilities of the present data warehouse leading to creation of new products and services or improvement of the current process.
  • Processing of data sets can be considered as a propaedeutic step using data lakes before ingesting them into the data warehouse.
  • Smooth functioning with streaming data.

In a nutshell, it can be concluded that the data warehouse remains to be a critical component of the enterprise data architecture. The data warehouse also ensures seamless functioning of BI tools and easy access of the required data for different stakeholders.

Do data lakes enhance your business?

Regardless of data quality or structure, the enterprise will have a higher access to the vast volume of stored data.
  • Elimination of processing before storage, makes it cost-efficient to store large amount of data.
  • The multi-purpose data can be borne at a low cost in the absence of restructuring it into various formats.
  • Easy identification of new use cases due to the flexible functioning of data through different applications and models.

However, as most of the organizations are on the move to transit data to the cloud, the selection of data warehouse or data lake does not become a concern. Instinctively, the organizations require to have both data lake and data warehouse to ensure flexible movement of data from the former to the latter, and thus enable business analysis.

Lastly, enterprises would have to analyze the complementary functions and benefits of both data lakes and data warehouses, to enhance effective exploration of data and to get the maximum output by employing both parameters.

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