Press "Enter" to skip to content

Category: DevOps

Release Flow Branching and Database DevOps

Kendra Little explains why the Azure DevOps Release Flow model can work well for database activity:

But how do you use branches? It’s helpful to pick a strategy. There are many fine Git branching strategies out there, things like GitFlow and GitHub Flow and more — enough that it’s overwhelming to learn about these when you are just starting out.

The strategy that I recommend for folks who are starting out with database DevOps and Git is the Azure DevOps team Release Flow model with dedicated development databases. (Why dedicated development databases? Read more here.)

Read on to learn why.

Leave a Comment

Using Azure DevOps for Power BI CI/CD

Marc Lelijveld and Ton Swart look at today’s CI/CD options for Power BI:

As a developer we might be used to working with Git repositories, especially in order to have release management in place. Git is well known as a modern version control system. By using Git, you will have a local copy of the code on your machine as well. Based on these local copies, you can continue developing. After you’re finished with your work, you can easily push your local repository to merge with the online (shared) repository. By doing this, only the changes will be pushed and saved in the online repository. In fact, only for the new code there will be a new version created. 

Versioning of Power BI files is a whole different story. Since pbix files are binary files, there is no way of checking-in only the code changes. The process of pushing changes identifies the pbix file as one object which has a new version.

Read on for the state of the art. To be honest, I don’t like the state of the art that much, but that has nothing to do with Marc and Ton’s great article.

Comments closed

Query Folding, Azure DevOps, and Power BI

Eugene Meidinger tries to work around a query folding limitation:

Query folding is one of the most powerful tools in Power Query and Power BI. It is the automatic process of pushing down filters and other transformations back to the data source. This can dramatically improve performance for your queries.

Unfortunately, OData is not guaranteed to support query folding. According to the Power BI documentation on incremental refresh.

Click through for Eugene’s alternative solution.

Comments closed

Scaling Out Continuous Integration

Chris Adkin shows off parallelism in Azure DevOps continuous integration pipelines:

A SQL Server data tools project is checked out of GitHub, built into a DacPac, four containerized SQL Server instances are spun up using clones of the ‘Seed’ docker volume. The DacPac is applied to a database running inside each container, which a tSQLt test is then executed against, finally, at the end very end the tSQLt results are aggregate and published.

This is an interesting approach to the problem of lengthy tests: run them on several separate machines concurrently.

Comments closed

Local Database Builds with Jenkins

Steve Jones continues a series on continuous integration, containers, and all that is good in life:

The only way to build a database project in SQL Server is with an actual SQL Server. In this case, I don’t have any code that would error on LocalDB, so I’ll just use that. I coudl specify my local SQL Server development database if I had the need.

This is a test build, so I also don’t need any SQL Compare options or other switches.

Getting code into source control and building continuous integration around it has become a lot easier over the past several years. Easy enough that you can work a simple system out in a day or two of experimentation.

Comments closed

The State of DevOps for Data Platform Professionals

Kendra Little summarizes the Accelerate: State of DevOps Report 2019 with a focus on what this means for data platform professionals:

While there are a ton of valuable insights in the report, in this post I will focus in on the findings which I believe are most relevant to those of us who work “close to a database.” There are three very interesting aspects of the research which hit close to home:

1. Speed and stability are not tradeoffs
2. Heavy change processes negatively impact speed and stability
3. Communities of practice are a common and successful tool to transform culture

Read on for Kendra’s detailed notes.

Comments closed

Requirements for DevOps Success

Grant Fritchey lays out what you need to succeed at DevOps:

The project managers and others flip. Delivery is slipping. The amount of code being written has changed. Stuff is happening that wasn’t on the schedule. The implementation of DevOps is shut down quickly.

You have to get buy-in from management before you attempt to implement DevOps or it will fail. They have to understand what you’re doing, why you’re doing it, and the measurable benefits it will bring.

Click through for some good thoughts, none of which is “use this software.”

Comments closed

Building an Azure DevOps Pipeline

Grant Fritchey shows off how to build an Azure DevOps pipeline:

I don’t mean for this to be a complete tutorial on setting up Azure DevOps (see the bottom of this post for my all-day, in-person, teaching sessions, where I will). I just want to discuss a set of pipelines I’ve built and why I built them that way as a method for illustrating how you can begin automating your processes in support of a DevOps implementation.

The most important concepts when we get to building and deploying within Azure DevOps are Builds and Releases. Yes, we can get into all the fun of talking about Deployment Groups (sets of servers for simultaneous deployment) and others, however, we have to first get a successful and then define how that build will get deployed/released; Builds and Releases.

Read on for the demonstration.

Comments closed

A Primer on Jenkins

Shubham Dangare gives us a quick walkthrough of setting up a Jenkins pipeline:

Jenkins Pipeline (or simply “Pipeline”) is a suite of plugins which supports implementing and integrating continuous delivery pipelines into Jenkins.

A continuous delivery pipeline is an automated expression of your process for getting the software from version control right through to your users and customers.

It provides an extensible set of tools for modeling simple-to-complex delivery pipelines “as code”. The definition of a Pipeline is typically written into a text file (called a Jenkinsfile ) which in turn is checked into a project’s source control repository

Click through for a demo.

Comments closed

Getting to DevOps

Grant Fritchey takes us through some of the baby steps in getting started with DevOps:

However, easy by comparison doesn’t mean just simply easy. There’s a lot of work involved and making mistakes early in the process has repercussions for every later step.

For example, where do your put your code?

Yeah, yeah, I know. Source control. I mean, where in source control do you put the code? What do you call the project and solution? Is it in git, github, Azure, or somewhere else? Choose wisely because every single step of automation you set up after this will be completely dependent on that first choice. Further, putting in github, or example, has repercussions for how you implement automation in Azure DevOps Pipelines.

But hey, no pressure.

Comments closed