A Fine Slice Of SQL Server
Richard Swinbank configures some deployments:
In earlier posts in this series, I talked about to developing and deploying standalone Power BI datasets and automating report deployment into different environments. I’ll bring together those approaches in this post, to enable deployment of shared datasets into multiple environments. This has consequences for automated report deployment, and I’ll take a look at that too.
Read the whole thing.
Comments closedGrant Fritchey explains why stateful systems are difficult to roll back:
The invitation this month for #PGSqlPhriday comes from Dian Fay. The topic is pretty simple, database change management. Now, I may have, once or twice, spoken about database change management, database DevOps, automating deployments, and all that sort of thing. Maybe. Once or twice.
OK. This is my topic.
I’ve got some great examples on taking changes from the schema on your PostgreSQL databases and then deploying them. All the technical stuff you could want. However, I don’t want to talk about that today. Instead, I want to talk about something really important, the concept of rollbacks when it comes to database deployments.
I completely agree with Grant’s description of the pain and his recommendation. With stateful systems, roll forward, not backward.
Comments closedI’ve used several migration frameworks in my time. Most have been variations on a common theme dating back lo these past fifteen-twenty years: an ordered directory of SQL scripts with an in-database registry table recording those which have been executed. The good ones checksum each script and validate them every run to make sure nobody’s trying to change the old files out from under you. But I’ve run into three so far, and used two in production, that do something different. Each revolves around a central idea that sets it apart and makes developing and deploying changes easier, faster, or better-organized than its competition — provided you’re able to work within the assumptions and constraints that idea implies.
Read on for thoughts about three tools: sqitch, graphile-migrate, and migra.
Comments closedRyan Booz relives an older story:
For those of you that don’t know, those ER tools were really expensive (probably still are for the ones that exist) and only a few developers had access to the tool. They didn’t have a great DX either.
Aside from the lack of automation and ability of our developers to be more integrated into the process, there was always the one looming issue that we just couldn’t reconcile.
If Joe left and joined the circus (see, I got you there), we were stuck.
We knew this was a bottleneck for some time and we had tried multiple times to change the process. Our ability to iterate on new feature development went through one person and a set of 15-year-old scripts. It didn’t match our otherwise Agile process of front-end code and data analysis projects.
Read on for Ryan’s thoughts on database change management. Some of the tools mentioned work with multiple database platforms, whereas others are specific to Postgres.
Comments closedRichard Swinbank deploys a report:
In the first post in this series, I built an Azure DevOps pipeline to automate steps in a Power BI development workflow. The pipeline implemented a very basic workflow – as soon as a developer committed a new report version to Git, the pipeline deployed it immediately into a Power BI workspace.
In this post I’ll be building a pipeline to support a more sophisticated workflow that enables peer review and stakeholder testing.
Click through for the step-by-step process.
Comments closedKevin Chant uses a deployment tool to install a deployment tool for his deployment tools:
I decided to do this post after some feedback I received about SqlPackage after a series of posts about deploying dacpacs to serverless SQL Pools. For example, my post about deploying a dacpac to a serverless SQL pool.
Because in order to deploy dacpacs to serverless SQL Pools you must update SqlPackage.
With this in mind, I thought I better go through various ways to update SqlPackage if intending to use it to deploy dacpacs to serverless SQL Pools.
Read on to see how you can do this.
Comments closedRichard Swinbank improves on a prior version control system:
In the previous post, I outlined a possible workflow for Power BI development, and implemented an Azure DevOps pipeline to show how steps in such a workflow could be automated. To build the pipeline I stored an entire
.pbixreport file – data and all – in version control, which is a problem for at least two reasons:
- storing large report files in a version control system won’t scale well
- datasets may contain confidential or sensitive data which must be kept out of version control.
In this post I’ll look at separating a report’s dataset from its visuals, version controlling the standalone dataset (without data), and deploying the dataset automatically to Power BI.
Read on for the process.
Comments closedOlivier Van Steenlandt publishes to Azure SQL Database:
During the step-by-step example, we will download the Pipeline Artifact created in my previous blog post. Afterwards, we configure the steps to deploy to Azure SQL. Near the end of this blog post, I will also include a link to my version of the YAML file.
Click through for those instructions.
Comments closedOlivier Van Steenlandt busts out the YAML:
In previous blog posts, I explained how to automate the Database Project Build & Deployment process using Azure DevOps (Release) Pipelines. These blog posts focused on setting up as easily as possible using the Classic Editor.
In this blog post, I’m going through the steps of setting up a build pipeline using YAML.
Read on to learn why the YAML-based approach is the best option for ADO and how to build a pipeline.
Comments closedStephanie Bruno does a bit of continuous integration:
There’s a lot of information on how to get around the lack of an out-of-the box CI/CD solution for Power BI datasets, but for me it’s often complicated and I have to read too many pages before making much progress on my own. This post is here to strip it down and provide you with the easiest way we know to enable a bonafide CI/CD process for Power BI datasets with Azure DevOps. The post is still longer than we’d like, but it includes detailed step-by-step instructions to walk you through every part of the process. To save space, we used slideshows for the screenshots, but you can pause them as you follow along.
There are a lot of steps but the goal is a worthwhile one.
Comments closed