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Category: Deployment

Azure DevOps Templates for Data Platform Deployments

Kevin Chant has some toys for us:

For my T-SQL Tuesday contribution this month is I want to introduce my Azure DevOps templates for Data Platform deployments.

This months T-SQL Tuesday is hosted by Frank Geisler. Frank has invited us to write about deploying SQL components through descriptive methods and build some new cool templates for them.

Which is good timing for me, because I co-presented a session on the day this post is published. I showed how to use YAML in Azure DevOps for Data Platform deployments at Data Platform Virtual Summit. .

Click through to learn more and see Kevin’s repos, as well as more information on the topic.

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Building a Deployment Pipeline with sqlpackage

Daniel Hutmacher builds a cloudless deployment pipeline:

I did some googling to see just how simple I could make a database deployment pipeline. I keep the DDL scripts in a git repository on the local network, but I can’t use Azure DevOps or any other cloud service, and I don’t have Visual Studio installed, so the traditional database project in SSDT that I know and love is unfortunately not an option for me.

So I googled a little, and here’s what I ended up doing.

Click through to see how Daniel was able to do it.

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Deploying SQL Server via Ansible

Amit Khandelwal gives us another way of deploying SQL Server on Linux:

Today, we’ll look at how to automate SQL Server deployment and configuration on Linux. To automate our deployment, we will use the Ansible system role, which is available here.

Note: The Ansible system role that I use in this blog is a sample system role that is provided as is and for reference only. Microsoft and RedHat do not support this. However, I invite you to provide feedback and suggestions for improving the system role here: Issues linux-system-roles/mssql (github.com).

Read on for the instructions and a demonstration.

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Deploying Azure Policies via Terraform

Jonathan D’Aloia shows how you can use Terraform to work with Azure Policies:

As you may all know Terraform serves a great purpose in deploying resources and infrastructure into your Azure environment, however, Terraform can also be used to automate and consistently deploy Azure Policies which can be defined prior to any resources being generated. In this blog, I will cover how you can import policies into your Terraform State to then deploy into an Azure Resource Group in order to secure your landing zone prior to deploying any resources.

Click through for an example.

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Data Platform Deployments via Azure Test Plan

Kevin Chant shows off the power of Azure Test Plans:

In this post I want to cover using Azure Test Plans for Data Platform deployments. Because using it to manage test plans can be very useful.

By the end of this post, you will know what Azure Test Plans are and how they can be useful for data Platform deployments.

Click through to see how this feature in Azure DevOps works and how you can use it to test your deployments.

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Content Sharing with Power BI

Marc Lelijveld continues a series on going from small-scale to enterprise with Power BI:

Let’s start with the most important feature of the Power BI Service, sharing content! At the same time, this can be one of the most challenging ones. Especially since there are many ways to share content in Power BI. In my experience in enterprise organizations, I have seen a various ways of sharing content. Below I explain the different options there are, leading to a conclusion of my personal best practice.

But why is the way how we share content so important in relation to large enterprise solutions? Well, I believe that all centrally managed solutions should match (organizational) best practices. The way how the content is made available to the users is one of these best practices. It will help end users to find the content they are looking for, always at the same consistent location.

Read on for several techniques.

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Renaming a YAML Pipeline in Azure DevOps

Hamish Watson figures out what’s in a name:

I had created a pipeline using YAML – which was called InfrastructureAsCode as the YAMP file was in the root directory.

However I wanted to move it into a folder .\InfrastructureAsCode\pipelines\… and run the YAML file from there – as I would have a non-prod and PROD version of them (as the schedule was different for each).

Click through to see how Hamish was able to resolve this.

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Creating a dacpac for a Dedicated SQL Pool

Kevin Chant shows how to use Azure DevOps to create a dacpac for an Azure Synapse Analytics dedicated SQL pool:

By the end of this post, you will know how to create a dacpac for a dedicated SQL Pool within Azure Pipelines for your CI/CD deployments. Plus, how you can synchronize a database project created in Azure Data Studio with a Git repository in Azure DevOps.

In a previous post I covered how you can create a dacpac for an Azure Synapse Analytics dedicated SQL Pool using Azure Data Studio. In that post I stated that you could create a dacpac for the database project using Azure DevOps.

With this in mind, I will use the same database project that I created in that post.

Click through for the process.

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The Value of a Working Dev Environment

Tim Mitchell wants to talk about dev environments:

Let’s talk about your development environment.

Specifically, I’d like to chat with you about the virtual space where your data architecture team, software developers, and information curators do their development and testing work. A proper development environment is logically separated from the production environment, and is often further partitioned into different realms for initial development, data or functional validation, and user acceptance testing. For mature enterprise-ready environments, there is also usually a build and deployment process that automates the movement of code from one environment to the next, reducing the chance for human error when moving code through its paces and ultimately into the production environment.

I’d like optimistically to say that Tim is using strawmen here, but I’ve worked in (and sometimes created) pretty much each one of these.

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