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Category: Deployment

Deploying an Arc-Enabled SQL Managed Instance

Published 2022-09-12 by Kevin Feasel

Warwick Rudd continues a series on Azure Arc-enabled data services:

Now that we have our Azure Arc-enabled Data Controller configured and available, we can now deploy our first Arc-enabled SQL Managed Instance into our environment. As previously mentioned depending on the type of configuration required for your environment with your Arc-enabled Data Controller (Directly connected or Indirectly connected modes) this will dictate the approach available for you to setup / configure your Arc-enabled SQL Managed Instance.

Click through for a step-by-step guide.

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A Primer on Azure Arc-Enabled Data Services

Published 2022-08-18 by Kevin Feasel

Warwick Rudd has a four-parter on Azure Arc-Enabled Data Services. Part 1 sets the stage:

Utilising Azure Arc-enabled data services provides you the ability to take advantage of the Azure data services (SQL Server, Azure SQL Managed Instance, PostgreSQL) in a hybrid environment. This offering provides you with reduced administrative efforts in managing and maintaining your data services while giving you the same look and feel as if you were running in the Azure Cloud.

Part 2 looks at the Data Controller:

The Azure Arc Data Controller is a Kubernetes operator that performs all of the orchestration to ensure you achieve your desired state. This is the main component in the Azure Arc infrastructure that links the data services with the Arc-enabled hardware located either in your On-premises, Azure, or any other public cloud data center and your azure subscription.

The Arc data controller allows you to deploy, manage, secure, and monitor your deployed data services estate using Azure Data Studio or the Azure Portal (only for directly connected mode deployments) but giving you the same experience as if you were managing your data services from inside of the Azure Portal.

Part 3 deploys a Data Controller:

As previously mentioned there are 2 types of deployment available for your Arc Data Controller. In this post, we are going to have a look at deploying in the Arc Data Controller using the directly connected mode.

For a directly connected Arc Data Controller, we have direct connectivity to our Azure subscription. With this in mind, there are several options as we previously discussed on how to deploy the data controller. For this post, we are using the portal deployment method.

Finally, Part 4 covers management options:

With ADS open and running you can create connections to Arc Data Controllers the same as you can with Instances of SQL Server. In ADS we have under the connections area a section specific for Arc Data Controllers.

Check out all four posts.

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Hosting an App on RStudio Connect

Published 2022-07-27 by Kevin Feasel

Liam Kalita wraps up a series:

So far, we have seen how to create an app using ReactJS and and a Plumber API. In part 3, we will show you how to host the application on RStudio Connect (RSC)!

When it comes to hosting the application on RSC we will set the content URL for both the app and API so that they are in the same domain and won’t have this CORS issue.

Read the whole thing.

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Updating Synapse Linked SQL Servers with Azure DevOps

Published 2022-07-27 by Kevin Feasel

Kevin Chant makes a change:

This post covers how to update both ends of Azure Synapse Link for SQL Server 2022 using Azure DevOps. As shown at the Data Toboggan conference.

By the end of this post you will know how to deploy database updates to both the SQL Server database and the Azure Synapse dedicated SQL Pool that are used as part of Azure Synapse Link for SQL Server 2022, using a pipeline in Azure DevOps. To keep them consistent.

Click through for the process.

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Choosing Azure DevOps or GitHub Actions

Published 2022-07-26 by Kevin Feasel

Sarah Dutkiewicz compares and contrasts:

Every time I do an Azure DevOps talk, I get someone asking me about migrating from GitHub to Azure DevOps. Every time, I have to ask “Why do you want to migrate from GitHub to Azure DevOps?” Why would you choose between Azure DevOps and GitHub? Or better yet – do you have to choose between them? Let’s look at how they compare and the tooling available.

This is a really tough question and Sarah helps explain why.

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Unresolved Reference to Object using Linked Server or External Table

Published 2022-07-20 by Kevin Feasel

Jose Manuel Jurado Diaz works around an issue with sqlpackage:

We used to have cases when our customers are trying to export their database using SQLPackage or Export service in the Portal having, for example, the following error message: Error SQL71562: Error validating element Synonym: [dbo].[MyView] has an unresolved reference to object [linkedserver].[databasename].[schemaname].[tablename].External references are not supported when creating a package from this platform. In this post I would like to suggest an alternative to export this data.

Read on to see what sqlpackage does not support, how you might solve it, and Eitan Blumin’s very good comment.

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Database Project Versioning and Identification

Published 2022-07-12 by Kevin Feasel

Eitan Blumin answers an important question:

“What is SSDT“, you ask? Oh, you didn’t? Well, let me tell you anyway! SSDT is the go-to solution from Microsoft for versioning SQL Server databases and performing state-based deployments (and it’s free!). It has many useful capabilities for developing and publishing changes from your SQL Database project to your SQL Database in production (or wherever).

One of the things that are not so clear about SSDT specifically and database versioning in general, is how should one identify which “version” of your database project was last deployed to your server?

Eitan includes several ways of tracking and controlling database versions.

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Databases, Applications, and Source Control Repos

Published 2022-07-05 by Kevin Feasel

Eitan Blumin asks and answers a question:

Following the rise in popularity of DevOps for Databases, many interesting questions are being asked on the topic.

One of these questions is: Should your SQL Database project be in the same source control repository and solution as the App code project? Or maybe they should be in the same repository but separate solutions? Or maybe they should be in completely separate repositories?

Pre-registering my answer here: for most organizations, databases should be in a separate repository. The deployment cadence is different, the deployment mechanism is different, and the people working on each likely differ. Read on for Eitan’s thoughts, which get into more of the nuance behind the answer.

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Git Native Support for Databricks Workflows

Published 2022-06-23 by Kevin Feasel

Vaibhav Sethi and Roland Faeustlin make an announcement:

We are happy to announce native support for Git in Databricks Workflows, which enables our customers to build reliable production data and ML workflows using modern software engineering best practices. Customers can now use a remote Git reference as the source for tasks that make up a Databricks Workflow, for example, a notebook from the main branch of a repository on GitHub can be used in a notebook task. By using Git as the source of truth, customers eliminate the risk of accidental edits to production code. They also remove the overhead of maintaining a production copy of the code in Databricks and keeping it updated, and improve reproducibility as each job run is tied to a commit hash. Git support for Workflows is available in Public Preview and works with a wide range of Databricks supported Git providers including GitHub, Gitlab, Bitbucket, Azure Devops and AWS CodeCommit.

Read on to see how it works.

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Creating Reproducible Examples with CI

Published 2022-06-03 by Kevin Feasel

Colin Gillespie and Jack Walton tackle a common training problem:

As the number of courses we offer increased, so did the maintenance burden of our associated training materials (lecture notes, slides, exercises, and more). To ease this burden, and to assist in ensuring that our training materials build consistently, we developed an R package called {jrNotes2}. Amongst other things, this package ensures that all courses:

– have identical “template files”: .gitlab-ci.yml, .gitignore, Makefiles, index.Rmd, …;

– have the same directory structure, and

– pass a set of quality-assurance checks.

This is smart but read on to see why it’s still a challenge. This is especially true in the R and Python worlds, where breaking changes seem to be so common.

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