Data Lake vs Data Warehouse | Top 14 Differences You Should Learn

Data Lake Vs Data Warehouse: What Is Different?

As businesses are becoming more and more omnipresent effective data management has become crucial for businesses to operate faster and develop a workable governance process to scale data for plans. Scalable and versatile data helps your business to accomplish tough tasks in minimum time. In the big picture, the formation of a successful data management ecosystem is the key for companies to address their customer needs in time as well as mitigate business challenges. The Revolution of technology in data science has further paved the way to adopt and integrate new data streams into your business models for smooth functionality. Where data warehouses have been in the story for quite some time now and have become a significant part of the IT landscape of companies. Similarly, Data lakes are ow creating more room for the data management domain in a great way. Let us understand some of the key differences between data lake and data warehouse.


What is a Data Lake?


Data Warehouse vs Data Lake: Key Differences


A data lake is a large storage house for unstructured data. The data is not manipulated or stored in the way it comes from numerous sources. This allows critical analysis of data as per client requirements and for future business endeavors.


What are the Key Features of a Data Lake?


Data lakes have gained immense popularity over a few years. This is because of the spectrum of benefits they have to offer concerning data management. Some of the important features of data lakes are described as under –


Raw Data Storage


You need a structured format to make the data sensible enough to work on the data in a data lake that exists in an unstructured format or sometimes a semi-structured format.


All Types of Data


A data lake can plug in all kinds of data formats and these data formats can be used when times are needed. The unstructured data acquired from sources like social media, sensors, digital images or emails, etc can be stored within standard formats easily.


Abundant Storage Capacity


A data lake is a cloud-based solution, therefore it makes sure that there is an endless capacity to hold a large amount of data.


Low Cost


Since a data lake is very useful for low-budget storage solutions this is because the overall cost of maintaining a data lake solution is lower compared to a data warehouse.


Easy Data Processing


The purpose of a data lake is to store data in a structured and portable format to enable easy access to data whenever required for business analysis. A structure is put in a frame to extract only the required data. This process is also known as schema-on-read.


High Flexibility


A data lake is highly unstructured and flexible. It can be used to configure data faster.


Uses and Users


A data lake is used to undergo advanced data analytics since the data is highly unstructured. This assists data scientists to accomplish smooth business operations by driving useful insights.




With the emergence of big data, the significance of the Hadoop data lake also rose. Hadoop is an open-source framework that provides data processing and data storage in a well-distributed computing surrounding.


What is the Need for a Data Lake?


The existence of a data lake is owned by the data warehouse because it did not fully satisfy the need of enabling in-depth data analytics. As opposed to the limited schema of data warehouses, data lakes provide a refined view of data to data experts with any employable technique. All this can be done without defining traditional data structures easily. Hence, you can generate a variety of useful insights. Thanks to a data warehouse, a data lake came into existence when businesses strived hard to earn real-time data analysis regularly.


What is a Data Warehouse?


Data Warehouses vs. Data Lakes: Understanding the Differences - Cazena |  Cazena


A data warehouse is an operational data storage architecture that is specially designed to access data from transaction systems and other external sources. The data warehouse then combines data as per the business’s need to perform data reporting and analysis.


What are the Key Features of a Data Warehouse?


The data warehouse has been in the data processing sector for quite some time now. The high demand for structured data format by businesses has resulted in its existence. Certain key features of a data warehouse are listed below –


Data Storage


Using a data warehouse the data is stored in highly compact and structured data formats such that you can create pre-defined reports and conduct seamless data analysis by playing with the data in a safe environment.


Fixed Types of Data


Data formats are then fixed based on specialized data analytics tools which can be plugged into a data warehouse. A good deal of thought processing goes into what type of data can best answer customer queries that pop up all the time. Therefore, a data warehouse proves very beneficial to structure and format data and maintain professional data upkeep.


Large Storage Capacity


You get more storage capacity with a data warehouse since there is a well-defined mechanism to perform tasks and respond to customer queries quickly still data warehouse is not as huge as data lakes. Graphical images, sensor data, social media data, etc are left out in the data warehouse because they need much larger space for storage.




Data warehouses are expensive to maintain in comparison with a data lake. The increased maintenance cost depends upon the complexity of data analytics, the need for IT experts and the initial investment for establishing the data schema to generate responses for queries also adds up.


Data Processing


Data warehouse functions are based on the Extract, Transform and Load (ELT) process. This schema is usually applied when the data is written this procedure is referred to as schema-on-write.




With the highly structured nature of the data warehouse, the supervision of IT experts is necessary because often small changes can prove time-consuming.


Uses and Users


A data warehouse is a mechanism over which data analytics applications are carried out. These analytic tools support an easy-to-use UI and can be accessed by non-skilled business users as well. Therefore, a data warehouse makes data analysis advanced and data reporting simpler for all organizations.




With the rise of the significance of big data the importance of data warehouses also rose to new heights. It is a relational database management system that has both rows and columns of data for which data rules are predefined.


What is the Need for a Data Warehouse?


When small enterprises or businesses need a single version of truth then a data warehouse is a good option for keeping data homogenized, structured, subject-oriented, and all-time-available. A data warehouse serves as a base for all sorts of Business Intelligent (BI) tools that can be loaded to efficiently slice, consolidate and generate useful data reports that can serve your business customer relations perfectly.


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