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Data Lakehouse, Data Mesh, and Data Fabric 

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(NOTE: I have returned to Microsoft and am working as a Solution Architect in Microsoft Industry Solutions, formally known as Microsoft Consulting Services (MCS), where I help customers build solutions on Azure. Contact your Microsoft account executive for more info. That being said: the views and opinions in this blog are mine and not that of Microsoft).

There certainly has been a lot of discussion lately on the topic of Data Lakehouse, Data Mesh, and Data Fabric, and how they compare to the Modern Data Warehouse. There is no clear definition of all these data architectures, and I have created a presentation using my own take that I have been presenting frequently internally at Microsoft and externally to customers and at conferences. Hopefully these presentations, blog posts, and videos can help clarify all these data architectures for you:

Look for a blog post of mine in a couple months that will cover Microsoft’s vision and technology solution of a data mesh.

Presentation abstract:

Data Lakehouse, Data Mesh, and Data Fabric (the alphabet soup of data architectures)

So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric.  What do all these terms mean and how do they compare to a modern data warehouse?  In this session I’ll cover all of them in detail and compare the pros and cons of each.  They all may sound great in theory, but I’ll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I’ll discuss Microsoft version of the data mesh.

The post Data Lakehouse, Data Mesh, and Data Fabric  first appeared on James Serra's Blog.

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