Differences between data marts, data lake, data warehouse y data cube

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A data mart is a data repository designed for a particular set of knowledge workers. There is a lot of trend confuse a data mart with a data warehouse. Both are frequently used incorrectly as synonyms..


Photo credits: Agsandrew

But not only data mart and data warehouse are the only terms related to databases and big data what can we find? Databases have become more than just structured tables for storing and retrieving information.. The big data analytics tools transform databases into new analytical platforms.

Architecture is rapidly evolving and growing in response to the need for provide business intelligence for effective decision making.

To help understand all these terms we will try to define them and delve into their differences.

Data Mart and other Big Data architectures

Where do you keep your data? Can you choose between a data lake and a traditional data warehouse? Do you know the possibilities of the Data Mart? Do you want to discover the benefits of the data cube?

In the following lines you will find the answers to all these questions:

  • Data Mart: is a subset of the data stored in a Data Warehouse, intended to meet the needs of a particular business segment. This data classification area focuses on information, achieving a maximum adjustment to the purpose of the users of the business unit. Its main benefit is its contribution to the prevention of dismissals.
  • Data warehouse: a data warehouse is the means of connecting the database with the analytical needs of the organization. This repository is designed to encompass all the data resources of an organization.. Its structure facilitates data extraction, its treatment and its subsequent availability to the user. Among its advantages is the feeding of the Data Marts, as well as the processing and analysis layers directly.
  • Data lake: This storage approach takes advantage of the heterogeneity of data and its sources, enrich the analytical capabilities of the most specialized profiles of the organization. This is a more fluid approach than a traditional data store in which the data stores retain their original formats and structures.. Its strong point is unlimited scalability.
  • data cube: This application manages to place the data in arrays of three or more dimensions, allowing a greater visibility of all its attributes. The benefit of working with data cubes is that knowledge workers can rely on them to create volumes of data that allow them to drill down into information and drive discovery.

The rigidity of traditional information systems has been left behind, giving way to a new era where the integration of databases with Big Data results in flexible and highly versatile architectures, represented by the Data Mart, el Data Lake, the Data Cube and the Data Warehouse.

It is now feasible for any organization to create information systems that serve the purposes of all users, providing business intelligence in the conditions in which it is required and supporting business development.

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