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Category: Deployment

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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Integrating Power BI Deployment Pipelines with Azure DevOps

Marc Lelijveld shows how you can combine Power BI deployment pipelines with Azure DevOps:

Looking at the Power BI release plan, dataflow support for Deployment Pipelines is coming up shortly! Currently it is scheduled for June 2021 to reach the public preview state. Versioning and DevOps integration go hand-in-hand to our opinion. With Azure DevOps Git integration, we can overcome the versioning challenge while integrating with Azure DevOps at the same time, as described in the previous blog in 2019. Today, we release a new version of the DevOps implementation which uses native Power BI functionality. Stay tuned!

As we really like the metadata deployment and the ease of setup a pipeline in the Power BI Service, Ton and I decided to setup an Azure DevOps extension based on the recently released Power BI REST APIs for Deployment Pipelines. Although Microsoft promised to come-up with a native DevOps extension over time, we decided to go for it. Time to bridge the gap!

Read on for more details.

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Creating a dacpac for a Dedicated SQL Pool

Kevin Chant shows us how to build out a dacpac file for an Azure Synapse Analytics dedicated SQL Pool:

In reality, you can create a dacpac for a database that’s inside an Azure Synapse Analytics dedicated SQL Pool using a lot of the methods that you use to create them for SQL Server databases.

Azure Data Studio can be an appealing alternative SQL Server Data Tools (SSDT) for tasks like this. Due to various reasons. For instance, it’s a multi-platform solution that is easy to install.

With this in mind, I decided in this post to cover how to create a dacpac for an Azure Synapse Analytics dedicated SQL Pool using Azure Data Studio.

Click through to see how.

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Enumerating Breaking Changes to Power BI Reports

Brett Powell gives us a list of things which might cause breaking changes in Power BI reports:

A breaking change, which we can define as any change to a dataset which causes either reports to render errors or the dataset to fail to refresh, can severely impact business workflows and reflect poorly on those responsible for the solution. Given significant investments in other areas of the organization’s data estate such as Azure Synapse Analytics, a simple, easily avoidable oversight in a Power BI deployment may not be tolerated.

Read on for the list.

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Creating an Azure SQL Database from Powershell

Gijs Reijn shows how to automate the process of creating an Azure SQL Database using Powershell:

Before you can create an Azure SQL database, you must create an Azure SQL server to host it on. Assuming you’re already authenticated to Azure:

Open PowerShell on your local computer and create the Azure SQL server that will host the Azure SQL database.

The command below is creating an Azure SQL server called sqlestate in the prerequisite resource group with a SQL admin username of SqlAdministrator and a password of AVeryStrongP@ssword0. The command is saving the output of the New-AzSqlServer cmdlet to use attributes from the server created later.

Read on for the step-by-step breakdown and full script.

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Executing GitHub Actions via CLI

Kevin Chant uses the GitHub CLI:

In this post I want to share some advice about using GitHub CLI with GitHub Actions for Data Platform deployments. Because I showed that at SQLDay last week.

For those who were not aware, there is a GitHub CLI you can use from the command line. You can download GitHub CLI from here.

Anyway, GitHub CLI was recently updated to support commands for GitHub Actions. GitHub Actions is the CI/CD mechanism that is now available in GitHub. Which I have covered in a few other posts, including the one you can find by clicking here.

Click through to learn more.

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Using Database Projects for Declarative Database Development

Haroon Ashraf explains the principles behind database projects and demonstrates their use:

This article is all about declarative database development using Azure Data Studio for both beginners and professionals who are new to it.

Additionally, some professional life tips in the context of the topic are also shared. The importance of declarative database development over its counterparts can also be fairly understood by going through this article.

Conceptually, I love it. Focusing on the end state is easier to understand. The problem I run into is that the tooling for generating change scripts is not great. It works for trivial database sizes, but as soon as you start talking dozens or hundreds of gigabytes of data, database projects have a tendency to do rather drastic changes which require rebuilding the table, when they could (with a bit of human smarts) perform an action which is much less disruptive. So in the end, you still end up needing to create change scripts.

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Deploying from One Source to Multiple SQL Servers with GitHub Actions

Kevin Chant demystifies GitHub Actions:

In this post I want to share how to deploy from one source to multiple SQL Server database types using GitHub Actions. Because I did a demo of it at Data Saturday Redmond last weekend.

By the end of this post, you will know more about how to do this using GitHub Actions. If you are used to Azure DevOps, you will find this an interesting comparison.

Previously I did a post about how you can do this using Azure DevOps. You can read that post in detail here. Later in this post I also mention an older post here a couple of times so it’s worth keeping that open.

Read on to learn how.

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