Data Lake, Data Warehouse and Data Mart: The Difference Explained

The terms "Data Lake vs Data Warehouse vs Data Mart" are frequently interchanged. But what exactly are the distinctions between these things? A data lake is a repository for all types of data created in various aspects of your organization, such as structured data feeds, chat logs, emails, photos (of invoices, receipts, checks, and so on), and videos. The data collecting procedures do not filter out any information; for example, data pertaining to canceled, returned, and invalidated transactions will be recorded.


A data warehouse typically contains only modeled/structured data. A Data Warehouse is multi-purpose and intended for a variety of use-cases. It ignores the intricacies of needs from a single business unit or function. As an example, consider a company's Finance Department. They are concerned with a few metrics, such as Profits, Costs, and Revenues, in order to advise management on decisions, but not with others that marketing and Sales are concerned with. Even though there are some commonalities, the definitions may differ.


Both data lake and data warehouse are often used for storing huge data, however the phrases are not interchangeable. A data lake is a large pool of unstructured data with no clear purpose. A data warehouse is a storage location for structured, filtered data that has previously been processed for a particular purpose. The data lakehouse is an emerging data management architecture trend that combines the freedom of a data lake with the data management capabilities of a data warehouse.


These two methods of data storage are frequently mistaken, yet they are far more dissimilar than they are similar. The only true resemblance between them is their high-level purpose of data storage. The distinction is significant since they perform various functions and need the employment of separate sets of eyes to be adequately maximized. A data lake may be appropriate for one firm, whereas a data warehouse may be more appropriate for another.


Distinctions Between a Data Lake and a Data Warehouse


Data Mart is frequently confused with data warehouses, however the two serve quite distinct functions, as seen below:


 Assisting various data types: A data warehouse typically comprises of data retrieved from transactional systems and is composed of quantitative measures and the attributes that characterize them.


Non-traditional data types supported by a data lake system include web server logs, sensor data, social network activity, text, and photos. These non-traditional data sources have largely gone unnoticed since consumption and storage can be prohibitively expensive and complicated.


User Support: A data warehouse is an excellent choice for users that need to assess reports, analyze important performance indicators, or handle data sets in spreadsheets on a daily basis. As a result, a data warehouse is appropriate for "operational" users since it is straightforward and designed to satisfy their demands.


A data warehouse can also assist users who do more in-depth data analysis. They rely on data warehouses for data integration, data preparation, and data analytics. Users may also utilize data warehouses to do extensive analysis, which can result in the creation of whole new data sources based on research. These users are primarily referred to as 'Data Scientists,' and they employ complex analytical techniques such as predictive modeling and statistical analysis.


Data Warehouse


A data-warehouse is a multi-purpose storage facility for many use cases, whereas a data-mart is a subset of the data-warehouse that is planned and created exclusively for a certain department/business function.


Some Advantages of Employing a Data-Mart:


Isolated Security: Because the data-mart only includes data relevant to that department, you may be confident that no unwanted data access (financial data, revenue data) is physically feasible.


Isolated Performance: Similarly, because each data-mart is only utilized for one department, the performance load is effectively managed and shared within the department, preventing other analytical workloads from being impacted.

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