The Need For Multiple Warehouse Architectures

James Serra argues in favor of a data lake approach and a traditional data warehouse:

I think the ultimate question is: Can all the benefits of a traditional relational data warehouse be implemented inside of a Hadoop data lake with interactive querying via Hive LLAP or Spark SQL, or should I use both a data lake and a relational data warehouse in my big data solution?  The short answer is you should use both.  The rest of this post will dig into the reasons why.

I touched on this ultimate question in a blog that is now over a few years old at Hadoop and Data Warehouses so this is a good time to provide an update.  I also touched on this topic in my blogs Use cases of various products for a big data cloud solutionData lake detailsWhy use a data lake?and What is a data lake? and my presentation Big data architectures and the data lake.  

Read on for James’s argument, which is good.  My argument is summed up as follows:  the purpose of a data warehouse is to solve known business problems—that is, to help build reports that people on the business side need based on established requirements.  The purpose of a data lake is to hold all kinds of data and curate it for when people come looking for something they didn’t know they needed.

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