The Data Warehouse, also abbreviated as DW or DWH, is a solution that allows companies to centralize large amounts of information in one place and with the option of being able to analyze it in real time for decision-making. Tons of data and information are being generated every day that allow organizations to make decisions based on it. For this reason, technological solutions that allow the treatment and analysis of data are increasingly on the market. 

Architecture of a data warehouse

The architecture of a data warehouse can vary according to the organization and the specifications of each one, but generally it occurs in 3 levels:

Single-tier architecture

It allows the storage of a certain amount of information eliminating duplicate data to minimize the amount of this, this type of architecture is not recommended for an organization with very dense data levels.

Two-tier architecture

This architecture contains a filter between the source of the data and the final storage in which clean adjusts the formats of all the data.

Three-tier architecture

It is made up of three levels:

  1. Lower level: position in which the data is loaded when it is already clean and transformed.
  2. Intermediate level: allows more complex analysis and queries because it houses an online analytical processing server also known as OLAP.
  3. Top level: here the end user can access and interact with the data. Represents the front-end layer and enables reporting, querying, analytics, and data mining.

Benefits of a data warehouse

Data Warehouses have multiple benefits for organizations, such as:

Types of data warehouse

Enterprise data warehouses

Data Warehouses can be used in different fields for data storage and analysis, but an enterprise data warehouse is where all the data of an organization is gathered. This information can come from different sources and internal areas of the company and, once stored, all the information can be consulted and analyzed by all the members of the organization. The person or team in charge of analyzing this data will be able to identify patterns of behavior in the data or make predictions of these and then establish action plans and make decisions for the benefit of the organization.

Operational data warehouse

The operational data warehouse, also known by its acronym ODS, allows the information already stored to be crossed and taken to build operational reports, controls and decision-making. An ODS is updated in real time, which makes it more useful for daily inquiries.

Data market

A data market is focused on a line of work or a specific area of ​​the organization, such as commercial or finance areas. This information is already filtered and ordered so that the consultation is quick and at hand.

Differences between a data warehouse, data mart, data lake and database

The constant digital transformation that has occurred in recent years has allowed information and data technologies to advance and generate new tools dedicated exclusively to data analysis. Although all these concepts are interrelated, they have a different meaning and use within the ecosystem of data collection and analysis.

According to our need, there are multiple options that are available in the market today and that adjust to what we require. All these solutions are regularly connected, without affecting each other, providing data storage, ordering and analysis solutions that allow an organization to take advantage of all this information for its development and innovation.

A Data Warehouse is a solution that an organization with massive amounts of data must have within its portfolio of tools to be able to advance to business and artificial intelligence.

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