The Azure Data Architecture Guide has just been released!  Check it out: http://aka.ms/ADAG

Think of it as a menu or syllabus for data professionals.  What service should you use, why, and when would you use it.  I had a small involvement in its creation, but there were a large number of people within Microsoft and from 3rd parties that put it together over many months.  Hopefully you find this clears up some of the confusion caused by so many technologies and products.

“This guide presents a structured approach for designing data-centric solutions on Microsoft Azure.  It is based on proven practices derived from customer engagements.”

You can even download a PDF version (106 pages!).

The guide is structured around a basic pivot: The distinction between relational data and non-relational data:

Within each of these two main categories, the Data Architecture Guide contains the following sections:

• Concepts. Overview articles that introduce the main concepts you need to understand when working with this type of data.
• Scenarios. A representative set of data scenarios, including a discussion of the relevant Azure services and the appropriate architecture for the scenario.
• Technology choices. Detailed comparisons of various data technologies available on Azure, including open source options.  Within each category, we describe the key selection criteria and a capability matrix, to help you choose the right technology for your scenario.

The table of contents looks like this:

Traditional RDBMS

Concepts

• Relational data
• Transactional data
• Semantic modeling

Scenarios

• Online analytical processing (OLAP)
• Online transaction processing (OLTP)
• Data warehousing and data marts
• ETL

Big data and NoSQL

Concepts

• Non-relational data stores
• Working with CSV and JSON files
• Big data architectures
• Advanced analytics
• Machine learning at scale

Scenarios

• Batch processing
• Real time processing
• Free-form text search
• Interactive data exploration
• Natural language processing
• Time series solutions

Cross-cutting concerns

• Data transfer
• Extending on-premises data solutions to the cloud
• Securing data solutions