Data Lakes verstehen | Über den Mehrwert der Datenmengen

What exactly are Data Lakes and how do they work? As a result of the enormous data streams, the decisive question arises: How can added value be derived from the huge amounts of data?

Data Lakes play a decisive role in solving this problem. A data lake is a place where a company can store all structured and unstructured data. Due to the amount of information, the data lake can be used for flexible analyses in the Big Data environment.

The concept of Data Lakes
The Data Lake stands figuratively for a lake full of water. This lake is filled from a steady stream of data such as e-mails, Excel files and content from social media platforms. On the one hand, unstructured data such as raw data such as e-mails, PDF files, images, videos or social media articles are displayed, which do not have to be validated or reformatted before storage. Only when the data is actually in use is the structuring and, if necessary, formatting of the required data carried out. On the other hand, it contains structured data such as information in rows, columns or data records that have been sorted and processed with databases and data mining tools.

The water reservoir serves as a database in which the data is analysed. The water in the outlet contains the analysed data. Through this process, data can be sifted to derive useful business impulses marketing.

Data Warehouses vs. Data Lakes
If one speaks of the storage and provision of huge amounts of data, the term data warehouse is often used. However, the Data Lake and the Data Warehouse differ significantly in their concept and the type of data storage.

The Data Warehouse combines data from different sources and formats and structures the data to allow direct analysis. The data lake, on the other hand, takes data from different sources in raw format and stores it in an unstructured form in one place. The data brine does not need to know the subsequent type of analysis in order to store the data. When the data is actually needed, it is reformatted and structured.

The Data Warehouse focuses on key figures or transaction data. For example, images or audio files – the unstructured data – are not stored. Since the Data Lake provides the data in its original format, it can be used more flexibly. Accordingly, the data can be transferred into completely new structures and measured with new methods.

Creating competitive advantages
Due to the high amount of information provided, extremely meaningful and in-depth analyses are possible. This can result in important competitive advantages for companies. The exact analysis of sales transactions combined with customer opinions can decisively improve the price and offer policy.

Data Lakes: Fast & versatile
The saving process is very fast because the data is available in raw format. The ability to search through the large amounts of data is also very fast. The Data Lake offers more possibilities for the evaluation of the data than the Data Warehouse. However the Data Warehouse sorts out the information during storage that is not needed for later analysis.

Away from conventional data silos
Because Data Lake is able to incorporate scalability, agility, and flexibility, different types of data and analytical techniques can be combined to gain deeper insights. Traditional unconnected data silos and the data warehouse do not provide this feature. However, this feature is extremely important as the amount of data in the digital universe will increase tenfold by the end of 2020.

Data Lakes in practice
The main application areas of the data lakes are big data analyses. With the help of Data Lakes, the behaviour of customers can be predicted as accurately as possible in the future, since the connection between the reputation and purchase of the products under consideration and the purchase of similar products becomes apparent much faster. Thus, the customer can be better presented with suitable offers in the future. This is where the use of data lakes, artificial intelligence (AI) and algorithms comes in, because algorithms are able to include unstructured data in their evaluations.

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