Data Lake vs Data Warehouse: Difference Between Data Lake & Data Warehouse [2022]

Ever since Big Data came to the limelight, data lakes and data warehouses jumped into the scene. While both are data lakes and data warehouses are storehouses for Big Data, they are not the same. The only similarity between a data lake and a data warehouse is that they are used to store data. To understand these storage repositories’ unique purposes, it is essential to identify the difference between data lake and data warehouse. 

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Data Lake vs. Data Warehouse

Data warehouse

A data warehouse is a storage repository for large volumes of data collected from multiple sources. Before data is fed into a data warehouse, you must clearly define its use case. It usually contains both historical and present data in a structured format. The data stored in a data warehouse is used by businesses to create annual and quarterly reports to measure business performance. 

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Data lake

A data lake is a pool of raw data (data in its natural state) that flows like streams from data sources into the lake. Data lakes accept all data types, irrespective of whether or not it is structured or unstructured. First, the data is stored at the leaf level in an untransformed state, after which it is transformed, and schema is applied to fulfill the needs of analysis. Users can access the lake to dive in and take data samples to fuel business innovation.

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Data Lake vs. Data Warehouse: How are they different from each other?

Data structure

One of the biggest differences between data lake and data warehouse is the way they store data. While data lakes store raw and unprocessed data, data warehouses store organized and processed data. This is primarily the reason why data lakes require a larger storage capacity. By storing processed and structured data, data warehouses save valuable storage space and cut down costs.

The most significant benefit of data warehouses is that since they store processed data having a defined use case, businesses can readily use it for their organizational needs. Raw data also has a clear advantage – unprocessed data is highly flexible, making it ideal for ML tasks. However, since data lakes have no strict data quality and data governance measures, they can fast turn into data swamps. 


A data lake is characterized by minimal organization and filtration. Data can flow into a data lake from any source. Generally, individual data elements in a data lake don’t have a defined or fixed purpose. On the other hand, data warehouses store processed data that will be used for specific business purposes. Thus, data warehouses never store data that has no use within an organization. 


The ease of accessing data from a data repository depends on the storage structure as a whole. Since data lakes have no set structure or strict limitations, you can easily access and modify the data as and when required. Contrary to this, the architecture of a data warehouse is more structured. This is beneficial since processed data is easy to interpret and understand.

User base

Raw and unstructured data is pretty tricky to manage, analyze, and interpret. Data scientists and data analysts typically deal with raw data to extract meaningful patterns from it and transform them into actionable business strategies. Thus, data lakes require much more skilled and expert users who know the nitty-gritty of dealing with raw data.

On the other hand, you can easily visualize processed data in the form of charts, tables, graphs, spreadsheets, etc. This is why data warehouses have a more extensive user base – anyone having the basic knowledge of business data can work with data warehouses. 

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Perhaps the biggest issue of data warehouses is that they are not flexible or adaptable. It takes a significant amount of time, resources, and effort to modify a data warehouse’s structure, mainly because the data loading process is complicated. However, as the data always remains in its raw form in a data lake, anyone can access it anytime. You can explore and experiment with the raw data in any way you desire, without any restrictions. 

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Data lakes and data warehouses serve different purposes altogether. A data lake’s primary goal is to gather Big Data from disparate sources, whereas data warehouses are best for data analytics. While a data lake may work best for one organization, a data warehouse might be the best fit for another company, whereas some companies may require both. 

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What do you mean by a data lake?

A data lake is a data storage system that is used to store large volumes of data in its raw form unless it is needed. It is a pool of raw data (data in its natural state) that flows like streams from data sources into the lake. Data Scientists and Engineers are the primary users of the data lake. A data lake can also be used in association with a data warehouse as it can be used to dump all the raw data unless the warehouse is not set up. Companies that offer data lake for data storage include Azure, Amazon S3, and Hadoop.

Discuss the characteristics of the Data lake.

The following are the characteristics of the Data lake: Data lake retains all the data that has been used currently, previously, or might be used in the future. There is no expiry of the data so that the user can visit any data at any moment for the analysis purpose. It is extremely cheap in terms of storage as storing information in TBs and PBs does not cost much. Along with all the conventional data types, the data lake stores all the non-conventional data types as well such as web server logs, sensor data, social network activity, text, and images. These data types are stored raw and transformed only once they are ready to use.

What is a data warehouse?

A Data warehouse is a data storage system where we can store large chunks of data gathered from multiple sources. The data warehouses are widely popular among mid and large-scale businesses as a data storage and sharing system. Before data is fed into a data warehouse, you must clearly define its use case. Many organizations use data warehouses in order to guide data management decisions. Some of the popular companies that offer data warehouses for data storage are Snowflake, Yellowbrick, and Teradata.

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