Table of Contents

Top 5 distinctions between Data Lake and Data Warehouse


Before diving right into understanding the differences, we should learn about these two terms, Data Lake and Data Warehouse:- 


Data warehouses are central storage systems where data gets integrated from various sources. These repositories store current and historical data that is used to create annual reports for analysis purposes. A data warehouse is a well-structured and highly transformed storage house that can organize different subject materials and represent abstract images used by the organization.  


Whereas Data Lake is like a water bottle as it is clean, packed, and structured for consumption. Within Data Lake, data flows like a water stream, where users can have access to it to dive right in. All the data gets loaded in the data lake from different source systems in an untransformed state, and no data gets turned away. By applying schema, the data gets transformed to fulfil different needs of analysis.  

Lets’ see how data lake differentiates from data warehouse based on five key points:- 

Retain all data 

The development of a data warehouse takes a considerable amount of time because it gets designed to analyse all kinds of data and process it in a very structured manner for reporting purposes. But a large amount of data gets rejected if it doesn’t fit with the questions or the report. It automatically judges what data to store and which not to, making it a highly efficient storage system. Whereas, in a Data Lake, all kinds of data get stored, considering that to safe it for future use. The hardware used for data lake and data warehouse differs significantly.  

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Support all data types 

Data warehouse stores primarily transactional data extracted from the systems, but it does not support the non-traditional sources such as web server logs, sensor data, text, images, and social network activity. It is also a costly source to store data. In contrast, Data Lake embraces all sources of information regardless of their structure and transforms data only when its use is required. 

Support all users 

Different users want different needs; 80% of these users look for operational data that is stored and well reported. For these users, a data warehouse is an ideal storage system. Whereas, Data lake is ideal for those looking for various kinds of data and likes to go beyond that capability of computers, like data scientists who need to look at various annalistic or models to conclude. Though, Data Lake supports all types of users, whether they want metrics or create their reports.  

Adapt quickly to changes 

It’s not easy to make changes to a data warehouse because it fulfills specific requirements only. On the other hand, Data Lake is more accessible to users, and it is used in novel ways to answer questions. It is far easier to make changes in Data Lake, and through exploration, creative ways get found through data analysis.  

Provide fast and quick insights 

Data Lake can give faster results and more profound insights as it is in a pure raw data form, untransformed without the input of any development teamwork. As all types of data get stored within Data Lake, it is faster than the traditional data warehouse structures. A data warehouse typically works for those organizations that want to be in the driver’s seat to analyse data. They instead rely on a data warehouse to do their bidding.  

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