What Is The Difference Between Data Lake And Data Warehouse?

Business need to change if they are to keep pace with the many customer interactions that are taking place online and offline. To do this, they need reliable data analytics that allow them to respond quickly to new demands. One solution is a data lake that receives continuously updated data from different sources. In this blog, we will briefly describe the difference between data lake and data warehouse, along with it’s advantages and disadvantages in business.


The data warehouse is an indispensable information base for traditional company reports or audit assessments in medium and large companies. Structured data collected days, weeks or even months ago is often prepared and analyzed in an ETL (Extract, Transfer and Load) process.

This is no longer sufficient to react quickly to ever-changing customer behavior. Another option is to develop a data lake model. Before companies decide to implement data warehouses, the specific characteristics, objectives and above all, advantages and disadvantages of data warehouses and data lakes need to be carefully analyzed.

Advantages And Disadvantages Of Data Lakes

First, we will look at how companies can manage the volume of data they collect on a daily basis. What can be deleted immediately? What needs to be stored permanently? What needs to be done?

For security reasons, some companies initially want to archive all data until it is clear whether it is relevant to the business strategy. This is where data lake come in. In this case, data is stored in its original form until it can be used.

Data lakes are scalable, can act as a kind of cache for data warehouses, and are a low-cost way to store files in any format. This is particularly attractive for less structured data such as documents, images, emails and audio files.

Data scientists with expertise in business management and statistics have long been studying data lakes and developing ideas on how companies can manage new data sets, for example at different customer touch points.


Central Location

A data warehouse is a central location where information is collected from different sources in its original form and without further adaptation. There is no predefined structure for the location of data and data models are only the result of future scenarios.

However, data lakes also have their drawbacks. The unstructured nature of the data makes it difficult for companies to determine the necessary storage space and the most appropriate search tools for analyzing data from different systems and applications.

Another obstacle is the lack of experts to analyze unstructured data. They must first be trained or involved in the company’s staff and gain experience in initial projects.

In addition, integrating data from different sources is a challenge. In this case, it is advisable to test in small environments so that the results can be transferred to large and complex data sets.

Advantages And Disadvantages Of Data Warehouses

Although data lakes are gradually entering the productive data analysis environment of specialized departments in companies, data warehouses are still the standard for evaluating data from relational databases and business applications. Typical use scenarios of data warehouses are classic business intelligence and analytics applications, used for example for corporate governance.

A data warehouse provides tools for reporting, data analysis and long-term archiving of key business data. To date, there is no standardized method for migrating large volumes of data between data warehouse systems. Solutions that are not optimally designed will not cope with the integration of additional database sources. Unlike data lakes, data warehouses are also used to store aggregated versions of the same data in the form of structured reports.

With the growing volume of data, especially less structured data, companies are concerned that data warehouses are not able to provide the scalability and flexibility they need. In addition, traditional data warehousing solutions are reaching their limits in handling large volumes of poorly or inconsistently structured data and require fast response times to ad hoc queries.


Data lakes will not be completely redundant for data warehouses in the near future. The two approaches complement each other in the decision-making process. In this way, companies can overcome the limitations of existing capabilities and discover new opportunities. While both views are valid in the business world, the evolving digital landscape increasingly shows that data lakes (synonymous with a modern data warehouse) are better suited to companies looking to take the lead.


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