What is a Data Warehouse?

Nils Martens 5/16/2024

Gain an absolute overview of your company with a data warehouse.

Table of contents
  1. What is a Data Warehouse (DWH)?
  2. How does a Data Warehouse work?
  3. What is the structure of a Data Warehouse?
  4. What is the difference between Data Warehouse, Database and Data Lake?
  5. What are the 5 benefits of Data Warehouses
  6. What kinds of Data Warehouse solutions are there?
  7. Which Data Warehouse systems exist?
  8. Conclusion
  9. FAQ

The Data Warehouse acts as a single source of truth in a company. It provides key insights to management and other departments on many relevant data. This results in many advantages for your business. Instead of collecting everything yourself or having employees approach you through different channels in a decentralized manner, you centralize the history.

We'll tell you exactly what this looks like, which systems can help you and how a Data Warehouse works.

What is a Data Warehouse (DWH)?

The definition of a Data Warehouse is simple, but what really lies behind it is somewhat more comprehensive and cannot be explained in one sentence. In short - and at the same time translated - the term means 'warehouse data house'. There, all data from the most varied sources is collected and then you can query it. For example, in the form of analyses.

This allows you to make sound business decisions, particularly facilitated by historical data sets. All automated, with AI algorithms and equipped with several useful applications.

How does a Data Warehouse work?

Imagine a huge hall full of walls studded with numerous compartments and that to a great height. Each compartment contains information about your company, the market and other relevant areas. In the middle of the room is a display through which you can now query what you want, for instance, to make a business decision. Numerous robots will then collect all necessary information and prepare it for you. The information is logically ordered and assembled by artificial intelligence.

That was the visual representation. Convert it into a digital system and you get a Data Warehouse. Centralized, data processing, storing, managing and visualizing analysis. No matter how large the data volume is: thanks to intelligent algorithms, the software will always keep an overview, where human capabilities find their limits. Your results are inventory analyses and the ensuing potentials that give your progress a boost time and again.

Access to a Data Warehouse is via Business Intelligence Software (BI) or another type of analysis application. With it, you prepare your data from all departments for your decision-making.


What is the structure of a Data Warehouse?

To prevent a Data Warehouse from descending into complete chaos due to vast amounts of data, it follows certain rules. These can vary depending on the system, the company and the requirements of the users. The basis for this is the architecture of a Data Warehouse. Overall, there are four types of architecture.

  • Simple System - The basic system consists of the three levels of data mining and analysis creation, analysis of data in the Data Warehouse and the database server in which management and storage takes place.
  • Simple System + Staging Area - To better prepare data for storage, some systems use a staging area. This usually happens automatically via the installed system.
  • Hub-and-Spoke System - In this case, stations (Data Marts) are set up between the administration and user, catering to the varying demands of the company's departments. Thus, data packets are not just spat out in one direction, but where they are relevant.
  • Sandbox - As the name suggests, users can let off steam without rules in this part of the architecture. They are safe areas where new data parallels can be sought without impacting the actual system.

In all four structures, a Data Warehouse sorts at the third stage, the database server, a. o. by priority. If certain data packets are often retrieved, they are stored in quickly accessible stores. Data packets that are rarely of interest, on the other hand, go into a simpler storage infrastructure. This means resources are used effectively and querying relevant data is faster. In our previous image of a large hall, these would be further halls above. Highly frequented data comes down below (quickly accessible), less frequented further up (slower accessible). Thus, no process of the Data Warehouse is ever affected in favor of another.

What is the difference between Data Warehouse, Database and Data Lake?

So far we have dealt with visual representation (for simplicity) and the architecture. But what do databases and Data Lakes have to do with a Data Warehouse? At this point, we will go into more detail.

Data can land directly in a Data Warehouse, where they are analyzed and prepared for further processing by Big Data Analysis software or Machine Learning. In this case, the data does not need to be particularly well structured. Nor does it rely heavily on the tabular schema that characterizes a Data Warehouse. In that case, a Data Lake alone is sufficient.

Those who need it more precise let data first feed into a Data Lake or a database which then process data and sort it in the Data Warehouse stack. Subsequently, users receive more sophisticated reports. In this way, Data Lakes, Databases and Data Warehouses can work in tandem.

So, all three are linked, but their functionality is different. Whether they are used together or individually depends on the users who need the result. In short, the more data accumulates, the more logical the division of the load onto several systems seems.

Data Warehouse.png

The structure of a Data Warehouse with the intermediate stages of Staging Area and Data Marts.

What are the 5 benefits of Data Warehouses

Benefits help in decision making, whether a tool is necessary or not. Therefore, we want to reveal at least 5 benefits that a Data Warehouse brings with it.

  1. Data-based decision making is irreplaceable by any other method.
  2. All data from all sources can be found in one place.
  3. You can analyze data exactly for each business area.
  4. The data in the Data Warehouse is continuously analyzed and the course of change stored.
  5. The different tasks within a Data Warehouse are operated by different systems, thus strengthening the performance of each system. It's the collaboration, not the performance of a single software, that defines a DWH.

What kinds of Data Warehouse solutions are there?

When the first Data Warehouses emerged in the 1980s, they were still On-Premise solutions. They were inefficient and hence evolved towards other applications such as BI platforms. Thus, they paved the way for the transformation from simple storages into infrastructural, cooperative systems for data-driven reports and analyses.

As a rule, Data Warehouse systems are still preferably implemented on-premise within the company. The reasons are mainly safety and speed, which relate to the latency of data exchange. Alternatively, however, cloud-based Data Warehouse systems can also be used. They are especially beginner-friendly as they can be set up with just a few clicks. A cloud solution also has cost advantages as you only pay for what you need. In the following table, we will show you the individual advantages in a direct comparison between cloud-based and On-Premise Data Warehouse:


Which Data Warehouse systems exist?

No matter which direction you take, there are numerous software providers on the market offering suitable solutions. On OMR Reviews, you can find various systems that suit your company's infrastructure depending on your requirements. At the same time, you can get feedback from other users on each solution to find out in advance if they are also a match for your company.

The following 10 Data Warehouse systems are recommended by OMR Reviews users.


The structure of Amazon's Redshift Data Warehouse system.


We've tried to present the topic of Data Warehouse as simply as possible. But it is significantly more complex than just installing a CRM or HR Software. Various providers give you plenty of support to get started. Thus, especially cloud-based Data Warehouses are convenient for beginners.

However, the fact is as your company grows, so does the volume of data. Keeping track of this with Excel spreadsheets or simple tools is impossible. In addition, the requirements to manage and analyze data can vary for every area of the company. In this case, a Data Warehouse brings the right solution, it manages, stores and analyzes your data and grows with your company. It gives you the optimal opportunity to make data-driven decisions.


  1. What is a Data Warehouse?
    • A Data Warehouse stores, manages and analyzes the amounts of data that accumulate in your company. Thanks to a tabular schema, this data can be easily and AI-driven processed with an analysis tool, allowing you to make informed business decisions.
  2. What is the difference between Data Warehouse, Data Lake and Database?
    • The basic functional difference is key. For instance, Data Lakes can store unstructured data as well, which, however, limits further processing by Big Data Analysis software. A Data Warehouse is more complex and structured. Ideally, a Data Warehouse is combined with Data Lakes and Databases. They serve as the first instance before data is fed into the Data Warehouse.
  3. What Data Warehouse systems are there?
    • There are both On-Premise and Cloud solutions for Data Warehouses. Both have their advantages. On-Premise systems have the benefits of strong security and good latency, whereas Cloud systems primarily score with cost efficiency and beginner-friendliness.
  4. How does a Data Warehouse work?
    • A Data Warehouse stores a large amount of data that arises in a company these days, structures it, sorts and manages it using AI algorithms and given individualized rules. Then, the data can be presented to the user via integrated analysis tools.
  5. What are Data Marts?
    • Data Marts are nodes between Data Warehouse and end user. They sort the data in accordance to a relevant business area. Depending on the department, there are different requirements for data packets, which is why Data Marts are a great help in not having to analyze data exclusively uniformly.
  6. Where do I find the right Data Warehouse system?
    • At OMR Reviews in the category Data Warehouse you get an overview of suitable systems - rated by verified users and complemented with their experience reports.

Nils Martens
Nils Martens

Nils ist Gründer der Personal Branding Rebels und bereits einige Jahre im LinkedIn-Game unterwegs. Zusammen mit seinem Team unterstützt er Menschen und Unternehmen dabei, auf der Business-Plattform sowie darüber hinaus sichtbar zu werden. Die Rebels bilden dafür Mitarbeitende zu Corporate Influencern aus, übernehmen den Aufbau kompletter Personal Brands und educaten in Workshops.

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