Identify the best Azure SQL Database SKU for your on-premises database by using the Database Migration Assistant
Published Mar 13 2019 05:11 PM 6,014 Views

First published on MSDN on Aug 07, 2018

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Migrating your databases to the cloud is a complicated and time-consuming task that involves a lot of variables, such as what to migrate, how to migrate it, and the target and SKU to which you should migrate. Our goal with the Database Migration Assistant (DMA) is to answer these questions and to make your database migration experience simple and effective.

DMA currently supports assessments and migrations to Azure. In addition, DMA will support:

    • Azure SQL Database SKU recommendations, which identifies the best Azure SQL Database SKU for your on-premises databases (released August 2018).
    • Scale assessments, which allow you to perform assessments at scale to get target\Azure SQL Database SKU recommendations (due November 2018).
    • Azure target recommendations , which identifies the best Azure target for your databases - Azure SQL Database, Azure SQL Database Managed Instance, or SQL Server on Virtual Machines (due January 2019).

This blog posting focuses primarily on DMA’s Azure SQL Database SKU recommendations feature, which allows you to identify the minimum recommended Azure SQL Database SKU based on performance counters collected from the computer(s) hosting your databases. This feature provides recommendations related to pricing tier, compute level, and max data size, as well as estimated cost per month. It also offers the ability to provision all your databases to Azure in bulk. Note that this functionality will currently be available only via the Command Line Interface (CLI).

To get Azure SQL Database SKU recommendations and to provision the associated databases to Azure, perform the following steps.

1. Collect performance counters from the computer(s) hosting your databases.

The first step in this process is to collect performance counters for your databases. You can do this by running a PowerShell command on the computer that hosts your databases. DMA provides you with a copy of this PowerShell file, but you can also use your own method to capture performance counters from your computer. You do not need to do this for each database individually. The performance counters collected from a computer can be used to recommend the best SKU for all databases hosted on the computer. Capture performance counters for at least 40 minutes to get a meaningful recommendation, though the longer the capture duration, the more accurate the recommendation. After you have collected the performance counters, you can use DMA to identify Azure SQL Database SKU recommendations.

2. Get Azure SQL DB SKU recommendations using DMA .

Use the performance counters output file from the previous step as input for DMA. DMA will provide you with recommendations for the Azure SQL Database pricing tier, the compute level, and the maximum data size for each of database on your computer. DMA will also provide you with the estimated monthly cost for each database. Please note that the recommended value is the minimum SKU required for your queries to run in Azure with a success rate similar to your on-premises databases.


3. Provision your databases to Azure using these recommendations.

With just a few clicks, you can use the recommendations from the previous step to provision target databases in Azure to which you can migrate your databases. You can also make changes to the recommendations by selecting the specific subscription and server that you want the databases provisioned to in Azure.


You can perform all the steps in this process on a single computer or you can perform it on multiple computers to determine SKU recommendations at scale. DMA makes it a simple and scalable experience by supporting all these steps through the CLI. Support for this feature via the DMA user interface is planned for delivery later this year.

For additional detail, see the article Identify the right Azure SQL Database SKU for your on-premises database.

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‎Mar 22 2019 06:27 PM
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