Do You Actually Need a Data Lake?

Data lakes have become the cornerstone of many big data initiatives, just as they offer easier and more flexible options to scale when working with high volumes of data that’s being generated at a high velocity – such as web, sensor or app activity data. Since these types of data sources have become increasingly prevalent, interest in data lakes has also been growing at a rapid pace, as can be seen from this Google Trends chart:

However, as with any emerging technology, there is no one-size-fits-all: a data lake might be an excellent fit for some scenarios, but in other cases, sticking to tried-and-tested database architectures will be the better solution. In this article we’ll look at five indications that should help you understand whether it’s time to join the data lake bandwagon or if you should stick to traditional data warehousing. But first, let’s set the parameters of the discussion by defining the term ‘data lake’. 

Data lakes are excellent for storing large volumes of unstructured and semi-structured data. Storing this type of data in a database will require extensive data preparation, as databases are built around structured tables rather than raw events which would be in JSON / XML format.


Also read : what is lanman server

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