The differences between a data warehouse and a data lake has been discussed a lot as for example here and here.
To summarize, the main point in my eyes is: In a data warehouse the purpose and structure is determined before uploading data while the purpose with and structure of data can be determined before downloading data from a data lake. This leads to that a data warehouse is characterized by rigidity and a data lake is characterized by agility.
Agility is a good thing, but of course, you have to put some control on top of it as reported in the post Putting Context into Data Lakes.
Furthermore, there are some great opportunities in extending the use of the data lake concept beyond the traditional use of a data warehouse. You should think beyond using a data lake within a given organization and vision how you can share a data lake within your business ecosystem. Moreover, you should consider not only using the data lake for analytical purposes but commence on a mission to utilize a data lake for operational purposes.
The venture I am working on right now have this second take on a data lake. The Product Data Lakeexists in the context of sharing product information between trading partners in an agile and process driven way.
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