Data Lake vs Data Warehouse

Enterprises everywhere, big and small, are experiencing exponential growth in data. Today, the main asset owned by some of the largest corporations in the world is data. This exploding growth in data naturally requires organizations to seek and adopt modern data storage and management constructs. 

Data Warehouse was the de facto answer for enterprises for the past few decades. However, over the last decade, with the rise in the amount of data and the appetite to extract insights from this vast amount of data, the industry has seen a steady rise in the data lake concept. Data Lake and Data Warehouse are complementary data management strategies with different use cases. 

In this post, we’ll highlight the key differences between the two – data lake and data warehouse – so that you can make a well-informed decision on when to use data lake vs. data warehouse and get your data architecture right.

Data Structure and Storage: Data Lake Stores Raw, Unprocessed Data

The primary difference between a data lake and a data warehouse is the change in the data storage structure. Data lake accepts all types of content irrespective of the form, structure and origin. 

Data from any source or location can be easily saved in the data lake. It mainly stores raw data which has not been previously processed. Due to this reason, data lakes need an extensive storage repository to accommodate large data volumes.

Data warehouse takes data related to the quantitative metrics or acquired from transactional systems. The Data warehouse stores processed and refined data without any extra space demands. 

Data Lifetime: Data Lake Prefers To Reserve Complete Data Records 

Data lakes keep historical data that may no longer be needed or useful. It can retain nearly all kinds of data, including the one in current use and the data expected to be used in the future. It can house unlimited data forever.

The Data warehouse only keeps the currently required and relevant data for development purposes in the store. It doesn’t maintain hierarchies or long chains by keeping data that may never be needed.

Data Transformation Schema: Data Lake Modifies Data Only When Needed

Data lake captures unstructured, semi-structured, and structured data without altering its original form. Data lake transforms data components only when ready for processing. 

It follows the schema on read principle that allows storing data with no pre-defined schema. It defines the data design after adding data at the end of the process. No data requirements or modeling details are needed to be known in advance.

The data warehouse captures well-organized and properly structured data and works on already cleaned and transformed data. It follows the schema on write strategy that allows storing data with a pre-defined schema. It defines the data design before adding data at the beginning of the process.

Data Analysis: Data Lake Offers Flexible Processing Capabilities

Data lakes contain unprocessed data in the original shape, which is flexible to mold in the advanced stages. Data residing in the data lakes can be instantly and thoroughly analyzed for nearly any objective. Therefore, the application of machine learning techniques is easy on raw, unfiltered data.

The Data warehouse keeps processed data, which is easy to log but may get hard to analyze or change. Consequently, applying advanced analysis methods can be a time-consuming and challenging task. On the contrary, refined and filtered data is more understandable by the users.

Data Risks: Data Lake Are Prone To Clutter Unnecessary Data

Any unmanaged data storage with excessive data becomes less beneficial for the users. Data swamps can occur due to inadequate data quality and ineffective security protocols. A data lake forms data swamps when organizations don’t define clear guidelines and keep on dumping unrelated or unwanted data. 

Swamps lead to difficulty in accessing and analyzing data. However, careful data handling and management can prevent the formation of data swamps. Moreover, placing appropriate data governance principles can significantly improve data quality.

Data Purpose: Data Lake Is Better Suited To Answer Unknown Questions That Will Arise In the Future

The data lake requires no pre-defined or fixed intent for adding the individual data chunks in the storage. Data with undetermined or unclear objectives can be kept in a data lake without restrictions. 

On the other hand, data warehouse processes data to serve a specific purpose. Therefore, data warehouses facilitate organizations to use data for a defined role without wasting storage space and increasing complications.

Scalability Mechanism: Data Lake Scales Better – Both Technically and Economically – for Large Volumes of Data

The data lake has separate storage and computation capability with little to no dependency allowing reasonable storage within optimum cost. The disintegration between storage and computation supports businesses in utilizing raw data stored in various tiers to manage costs. Additionally, no ETL processing is required upfront, saving time and effort.

The data warehouse performs the ETL (Extract, Transform and Load) process initially. Tight coupling between the storage and computation makes the data warehouse scaling process complex and costly. Furthermore, scaling the computational capacity of a data warehouse may involve unnecessary upgrades like storage expansion.

Enterprise Agility: Data Lake Supports Agile Change Management

A data lake can flexibly store many data formats, including text, images, video, audio, PDF files, etc. It accepts nearly any data form without investigating the type. 

A data lake conveniently accepts and stores data without requiring the ETL process, leading to increased agility. Therefore, different departments of an organization with a data lake can push their data into the lake without making any substantial changes to the data and the lake. 

Datawarehouse requires following the ETL process before accepting and adding data into the storage, which is a time-consuming process. It has structured data that is not easily accessible due to rigid data control. 

Data is stored in a strict format under pre-defined design and format protocols. Any change to the format requires changing the warehouse process and the models relying on the system as well. Hence, achieving agility and making changes using a data warehouse is difficult for enterprises.

Audience: Data Lakes Cater To a Wider Audience

A data lake is ideal for data scientists and engineers interested in deep analysis, enabling them to conduct predictive modeling and statistical analysis. Data lake helps them gain valuable insights and make sound decisions.

The data lake caters to a larger audience than a data warehouse. Data scientists can use advanced and specialized tools for examining and understanding data for any business need. 

A Data warehouse is more appropriate for business professionals or analysts looking for easy-to-use and properly organized data. It stores aggregated data which results in loss of granularity. Like a data warehouse, a data lake supports setting up reports for business users.


Data lakes and data warehouses have been designed to serve different purposes. Both data management systems have exceptional features that can help solve varying data demands of organizations. A clear understanding of the data structure and company goals can assist in choosing the right one that fits the organizational needs. Regardless of the differences, both data management systems are equally important and useful for storing and processing data assets effectively.