What is Data Warehouse?

Understand the role of data warehouses (Data Warehouses) and their importance in the area of Business Intelligence.

If you follow our page LinkedIn, you've read our publications about the BI function and the which is data engineering, right? Well, for scientists and data engineers to structure their work, they need databases.

In practice, this is the function of a Data Warehouse: is a large database to store a large volume of information in a consolidated way. And to collect this large volume of data, systems called OLTP are used.

What are OLTP systems

OLTP (English acronym for Online Transaction Processing), is the term for transactional processing systems. These systems, in most cases, are the operating systems used by the company for processing routine data, and are generated daily – in most cases – to feed the Data Warehouse. 

We should not confuse OLTP with OLAP, as they are different concepts that complement each other.

What is OLAP

OLAP, also from English, Online Analytical Processing. In general terms, its usefulness is to perform analyzes and extract specific information from the information coming from the OLTP.

In a practical scenario, imagine that an airline needs to register all its customers. This will be done from one (or several) OLTP system. Later, when the company has a need for specific information (for example, only customers over 40 years old who have made more than 2 continental flights in the last six months), it will be extracted and analyzed by an OLAP system.

Okay, so what would be the need for a Data Warehouse if I can do OLAP within my own OLTP?

This is a good question and OLTP systems are becoming more intuitive and advanced, especially those related to CRM. The big advantage of using a Data Warehouse it's about centralizing data from multiple sources in the same place.

As large organizations (such as the airline example above) usually do not have a single data source, the role of DW is to bring everything together in the same environment and make that data and information speak the same language.

In this way, a lot of time is saved, because while in different OLTP there are different conditional rules, different forms of data extraction, different information organizations, in general, in a Data Warehouse the information is organized under the same logic. 

If we make an analogy with food, everything gets better:

• The different ingredients are the OLTP systems, this includes the various data sources, coming from CRM's, ERP's, operational systems, etc;

• The mixer or container for mixing them is the Data Warehouse. All together represent the physical structure such as hardware as well as specific software and applications;

• Oven time is OLAP, which includes the different ways to analyze and compare information from the Data Warehouse;
  

The cake, which is the final result, is the general objective of these tools. The same happens with the Business intelligence! The generated reports are the icing and decoration that make the cake even tastier. But we must remember that there was a whole previous effort to prepare this cake.

main objective of the Data Warehouse

Lastly, we must remember that the purpose of the DW  is to enable best practices for companies, reducing costs, optimizing profits and, above all, allowing better decision-making based on structured data.

And now, you are more familiar with the concept of Data Warehouse?

We hope so! If you have any questions, leave your comment. It will be a pleasure to talk. And remember to follow our blog and our Linkedin page to follow more posts about Business intelligence, Marketing and technology!

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