Data and information are becoming more and more important in companies. Data has developed into a key production factor for many enterprises in recent years, thus making the storage and utilization of this data just as crucial.
DWH versus Data Lake
Not only the sheer volume of data being created but also new kinds of data make the traditional management thereof using data warehouse concepts impossible. While with DWH concepts, incoming data is subjected to complex quality assurance and integration procedures before they can be stored in predefined structures (schema on write), data is stored as raw information in a data lake, and not converted and prepared until they are actually needed (schema on read). This means that large volumes of heterogeneous data can be stored far more efficiently, and specific data can be made available and linked together quickly and efficiently for analysis when required.
So Data Lake opens up entirely new possibilities for using data and information profitably in companies. But at the same time, what is becoming more important are questions of the source, trustworthiness, protection, and lifecycle management of the data.
The Data Lake Concept
The data lake concept opens up entirely new possibilities for using data and information profitably in the company. At the same time, however, questions about the source, trustworthiness, protection and lifecycle management of data are becoming increasingly important.
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