Admittedly, the data map was by far the hardest to build, because there is a big functional overlap across data services. Nevertheless, I tried to identify the primary use case of each service, or where a given service shines the most.
The purpose of the this map is to see, in a glimpse, which services may suit your functional needs but it is up to you to dig deeper.
Here is the map:
which focuses on the following areas:
Traditional: many enterprises still deal with traditional BI and there is nothing wrong with it! This category regroups Azure services which you can use to build your cubes, run your ETL jobs, etc.
Modern: this category is the counterpart of the traditional category. For example, ELT is the modern counterpart of ETL...that's a bit the spirit :). You may of course find services that are in both sides.
Big Data: Big Data is also recent in the data lanscape, so it could have been a subset of the modern group, but for sake of clarity, I decided to make it a separate group.
Artificial Intelligence: AI is on every lips so I couldn't skip it although this category was hard to craft. There is so much overlap across AI services that it's kind of hard to categorize them. I tried to have a very condensed group. AI would deserve to have its own map.
Others: in this category, you'll find concerns such as "sharing data with other companies", "Governing data", etc.
One note though: Microsoft is pushing hard on Azure Synapse Analytics and their aim is to have an all-in-one service, that combines decades of on-premises data practices and the most modern and top-notch data features. So, you'd better keep an eye on its development!