As the title of this post indicates, the Database Migration Service (DMS) could experiment with problems in very rare cases with the capacity to start/create a service due to not having enough resources in a specific region. It is interesting to start explaining why the provisioned number of Virtual Machines (VM) could impact DMS. If we ask Why? The answer is simple: behind the scenes, the VMs represent the “workers” in charge of executing ALL the tasks required by the migration service of DMS.

As every option available in Azure and particularly talking about SQL, for the classic question about What option is better for me? The classic answer is still valid and this is “It depends”.  Your necessity or your business workload or architectural design would influence in a determinant way with your decision.

In this article, I am going to explain in a sort of step-by-step guide an approach for configuring a new Azure SQL Managed Instance from now in advance called simply SQL MI.  It is so important to distinguish that exist different ways to arrive at the same result, but the most important is to understand the core concepts.

Historically the use of User Defined Functions aka UDF has represented a great option to encapsulate logic into Database reusable objects, nonetheless, during the implementation of scalar UDF (a variation based on the function’s output) for MS SQL Server we have been suffering painful performance penalties until the arrival of version 2019, so in this article, we are going to address how the new Scalar UDF Inlining feature helps us to avoid performance problems and get the maximum value in our Database code inside SQL Server.

First at all, I would like to clarify something that maybe sounds evident, but sometimes we forget, the path is not a string, it is a fact that we must keep in mind to avoid headaches in our journey in Python.  Since Python 3.4 in advance, we count on a powerful and versatile module called pathlib.