Just about every area of a modern business is digitized and networked these days. This allows companies to gain huge amounts of data, which can then be used again to increase efficiency, productivity and user experience. However, the mountains of data must first be organized and structured for this purpose. And this is exactly where the data warehouse comes in. We reveal how this technology can be optimally used and how it differs from a data warehouse.
The term data warehouse refers to a central database system that is used primarily for analysis purposes in companies. The task of the data warehouse is to collect information or data from heterogeneous sources, to prepare it, to store it in the long term and to provide further structures with analyses, the data marts.
A data warehouse solves one of the central problems of Big Data. It is true that companies can benefit in an incredible number of areas from the various data collected at a wide variety of points. However, this is only possible if the huge amounts of data can be organized and, above all, analyzed.
And this is where the data warehouse comes into play. Because this central database system collects the data from a wide variety of databases and then makes it available in a way in which the information can be efficiently queried. In this way, even companies with countless independent departments can obtain a comprehensive and global overview of their own business processes.
Data warehousing refers to the various processes that make up a data warehouse. In other words, the entire procurement, backup, management and provision of data. For ease of understanding, warehousing can be categorized into three different processes:
The first and most crucial process is data procurement. Data can be provided from a wide variety of sources, usually SQL databases. Through the so-called staging area, the data is extracted from the sources, structured and transformed if necessary. The information is then stored in the data warehouse.
This creates a parallel database to the original data sources, which in most cases do not have to be changed in their original state. The data can now be stored, modified and analyzed independently of the original sources. The goal of a data warehouse is that data can be stored there for a long time, theoretically indefinitely, as long as the data remains relevant.
Only the data supply or the preparation of the data takes place. The particularly practical thing about a data warehouse is that the data is not only summarized and securely stored here, but can also be sorted, structured and even analyzed according to the user's requirements. Using so-called data marts, the results of the analysis are summarized in such a way that they can be optimally used for data mining.
Data mining does not directly belong to the process of data warehousing and can also be operated independently of it. However, the aforementioned data marts often form the basis for data mining, which is why we would like to take a closer look at this process here. According to the definition, data mining is the systematic application of computing methods to find correlations, cross-references and trends in data sets.
The great advantage of data mining is that it can often be used to find relationships and trends in databases that the user was not originally looking for, or did not even know existed. In combination with real-time data transfer, a company can make the right decisions many times faster and more effectively.
Now it also becomes clear why the data warehouse is so important for data mining. Instead of users having to comb through the diverse databases themselves using computing methods, they now have access to a central data warehouse where, in addition, the data has already been pre-sorted and checked for quality.
Data warehouses, in conjunction with a method such as data mining, thus enable the user to sort out the information that is really important from the unlimited flow of data. A large part of the process runs automatically. Data warehouses will therefore become all the more important as Big Data and the networking of our everyday lives and the corporate world continue to advance.
The data warehouse has a decisive disadvantage: The data that can be collected and utilized there must be as heterogeneous as possible and originate overwhelmingly from databases. However, most of the data that companies acquire is in other formats, such as video, transactions and text formats.
That's why the data lake exists. Here, huge amounts of data can be stored in a wide variety of formats and made available for query. In the process, the formats of the data can be adapted to the particular query. The data prepared in this way could then, for example, be combined in a data warehouse for better analysis. Data warehouse and data lake are therefore not opposing systems, but rather complement each other in their function.