What is a data warehouse?

We explain what a data warehouse is, what you need to know about it and how it differs from other forms of data storage. For the sake of simplicity, we will only touch on the technical aspects very briefly.

With the rise of data science, a new jargon has blossomed in business in recent years. Whereas previously we used to talk about paper archiving and Excel files, we now have to deal with data storage. Terms such as data warehouse, data lake, datamart, or database tickle your ears all day long and you sometimes find it hard to keep up with the pace of its data specialists? Do not panic, you have come to the right place!


What is a data warehouse?

A data warehouse is a centralized system for storing data from different sources (most of the time a set of databases). This data is usually organized and structured in such a way that it can be processed and analyzed. However, there are also data warehouses containing unstructured data.


The data is imported into the data warehouse at regular intervals using an ETL (Extract Transform Load) process. Users can then access and evaluate the data. The need to create a point of access to all relevant data in order to use, manipulate and facilitate decision making has encouraged the emergence of data warehouses. 


The data warehouse may exist on site, which implies the mobilization of a number of resources and often specific software (proprietary or offered by a third party) or in the cloud. Several companies offer data warehousing services. This is the case of Amazon (Amazon Red Shift,) Microsoft or Snowflake.

What is the purpose of a data warehouse?

A data warehouse is an essential element in the data architecture that allows the centralization of heterogeneous data from various sources. It is well known that companies are faced with an ever-increasing flow of data, and this data is of no value if it is not used or if it does not help in decision making.


The creation of a structured and scalable data architecture system is therefore crucial for many companies. The data warehouse is a key component of this system. 


The data warehouse is generally structured around three distinct levels:


  • Data extraction and collection, or the ETL process. Extraction can be done automatically, using algorithms, or by manual intervention in some cases (to be avoided as much as possible);
  • Data archiving and organization of the data, i.e. the organization within the data warehouse so that the relevant data can be accessed quickly. This includes the possible creation of datamarts (we will come back to this later);
  • Providing access to the data, i.e. the interface with the users. In general, access is provided on a read-only basis.

Difference between data warehouse and data lake

Recently, the term data lake has become very fashionable. Data lake is like a large reservoir of raw data. The data is simply stored there without prior manipulation so that it can be used later on.


To give you a more intuitive comparison, the data lake can be compared to snow-covered mountains. It is potentially a prime ski area, but without ski lifts and slopes, few skiers will be able to take advantage of it. Thanks to the data warehouse, slopes are traced, and chairlifts are built in order to better organize the data and allow their exploitation.

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Difference between data warehouse and datamart

In principle, the data warehouse exists at the level of the company as a whole. It is a centralized data warehouse. In order to enable faster data processing, organizations often choose to create datamarts that are intended for a particular department or a specific audience. These datamarts therefore contain only the data relevant to these targets, which generally results in a reduction in processing time. The datamarts are presented as separate elements of the data warehouse or, in some cases, as simple subdivisions.


To take up the analogy of the ski area, datamarts divide the slopes into categories: red, black, blue, green, etc. Skiers can therefore choose the slopes that suit their level in this case (and their skills in the case of datamarts).


Datamarts also make it possible to partition access to data. Providing unlimited access to all the data collected by the company to everyone seems to be ill-advised. 

Difference between data warehouse and database


Databases or databases were in a way the first form of data organization. Data is structured within relatively rigid databases that generally only store the most recent values.


The data warehouse is designed to store a much larger amount of data than a traditional database. The objectives of the two storage systems therefore differ. The database is designed to store data, whereas the purpose of the data warehouse is to analyze and use the data for decision-making purposes.


If we go back to our snow-covered mountains, the databases passively collect snow, sunshine, and other information, whereas the data warehouse is used to make weather forecasts or determine which trails need to be cleared.

Store the data, and then what?

Whether we are talking about a data warehouse, data lake, database or datamart, these elements are only tools for optimal exploitation. Keeping data without exploiting them is like creating a vegetable garden to let fruits and vegetables rot... Organizing beautiful rows and a sophisticated irrigation system, keeping insects away or protecting the harvest from bad weather loses all its meaning if your carrots end up in the compost, or even worse, in the trash!


At Ryax, we have created a SaaS to deploy, run and scale data processing models. Want to learn more about our product and how Ryax can help your business? Contact us to schedule an appointment or discuss your requirements.


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