A Fine Slice Of SQL Server
In this video, we dig into GitHub’s process for executing code: GitHub Actions workflows. We’ll learn what Actions and workflows are, how we can create them from scratch, and how to incorporate Actions from the GitHub Marketplace into our own workflows.
Along the way, I describe what GitHub Actions workflows are and we build a simple one. I’ll have more videos coming up that expand on GitHub Actions and show you more of what you can do with them.
Comments closedBen Johnston builds out feature flags:
My motivation for writing this summary was an interaction with a project owner that didn’t understand why we couldn’t use feature flags directly in Power BI to control the user interface. This was different from our other deployments, so it took a few rounds of explanations to convince them that our use case didn’t support feature flags. It’s an oversimplification to say they can’t be used in data projects. They can be used in Power BI and other reporting tools, but the implementation is different from coding languages and their usage is limited in comparison. Feature flags can also be used in ETL tools, data engines, ETL tools, and other data tools, but with some caveats. Sometimes those caveats are severe enough that you will want to carefully consider how you use feature flags in your data projects.
Read the whole thing. The way Ben lays things out reminds me of why I historically haven’t been the biggest fan of feature flags, though they can be quite useful for application development purposes.
Comments closedSoheil Bakhshi has some thoughts:
In a previous post, I shared a comprehensive guide on implementing Incremental Data Refresh in Power BI Desktop. We covered essential concepts such as truncation and load versus incremental load, understanding historical and incremental ranges, and the significant benefits of adopting incremental refresh for large tables. If you missed that post, I highly recommend giving it a read to get a solid foundation on the topic.
Now, let’s dive into Part 2 of this series where we will explore tips and tricks for implementing Incremental Data Refresh in more complex scenarios. This blog follows up on the insights provided in the first part, offering a deeper understanding of how Incremental Data Refresh works in Power BI. Whether you’re a seasoned Power BI user or just getting started, this post will provide valuable information on optimising your data refresh strategies. So, let’s begin.
Read on for plenty of detail, including your available options and how to use them.
Comments closedJosephine Bush automates a deployment:
A well-structured Terraform module for Azure SQL DB typically consists of the following elements:
- Main Configuration Files:
main.tf,variables.tf,outputs.tf- Helper Files: (if necessary)
locals.tf,providers.tf, etc.If you want to learn more about the basics of Terraform, you can visit my previous blog post.
Click through to see how Josephine has put together the Azure SQL Database deployment module.
Comments closedKevin Chant checks in a Power BI project:
I decided to test the guide with the Power BI report that I showed in my post about work with Microsoft Fabric Git integration and multiple workspaces.
So, I went through the guide and was pleasantly surprised that it showed how to do it with a YAML pipeline in Azure Pipelines. Which I must admit I prefer for reasons that I covered why in a previous post about disabling classic pipelines in Azure DevOps.
Read on for a review of the issues Kevin had to sort out, as well as two mechanisms to improve the performance of your Azure DevOps CI process.
Comments closedAmit Khandelwal packs a lot into a post:
It’s time for a new blog on how Ansible can simplify SQL Server deployment, configuration, and availability. If you’ve read my previous blogs on Ansible for SQL Server installation and configuration, and the pacemaker-based Always On availability group, you know how powerful Ansible can be. Now, let’s talk about HPE Serviceguard for Linux (SGLX), a high availability/disaster recovery solution that provides business continuity for critical applications like SQL Server.
Deploying SQL Server Always On availability groups on HPE SGLX is a fully supported solution for production workloads.
Today, let’s look at how you can configure Always On availability group based on HPE SGLX via Ansible. We have collaborated with our friends in HPE to enable the Ansible based deployment for HPE SGLX with SQL Server. This feature is now available for HPE SGLX 15.10. For this demonstration, you can download the evaluation bits from the ‘My HPE Software Center‘. The Ansible bits with the scripts are available on GitHub:
Read on for instructions and what you need to make it all work.
Comments closedGeorgia Atkinson wraps things up with a bow:
Python is a general purpose, high level language which, thanks to its simplicity and versatility, has become very popular, especially within the data science community. The extensive Python community has developed and contributed thousands of libraries and packages over the years in a plethora of different disciplines to aid developers with their applications. Managing these packages can be a challenging task without the correct tools. That’s where Python package managers come in. In this blog post we will explore what a package manager is and why they are important. We will then cover some popular examples, including how to use them, how to install them and the pros and cons of each.
Whilst we will briefly touch on virtual environments in places, we will explore these in more depth in an upcoming post.
Read on for a primer on three options, including how they compare to one another for CI/CD purposes.
Comments closedDustin Vannoy covers one technique for CI/CD in Databricks:
Databricks Asset Bundles provides a way to version and deploy Databricks assets – notebooks, workflows, Delta Live Tables pipelines, etc. This is a great option to let data teams setup CI/CD (Continuous Integration / Continuous Deployment). Some of the common approaches in the past have been Terraform, REST API, Databricks command line interface (CLI), or dbx. You can watch this video to hear why I think Databricks Asset Bundles is a good choice for many teams and see a demo of using it from your local environment or in your CI/CD pipeline.
Click through for a video and some sample scripts.
Comments closedIt’s a bit of a challenge to keep up with all the changes, updates and all the new stuff coming out for Fabric. As I’m not really invested in the PowerBI part of the data platform (yay pie charts ;)), some things that are very common for the PowerBI community are very new to me. I have it on good authority that this blog covers a feature that is well know within PowerBI but quite new in the data engineering part. When I say that, I need to add that at the time of writing, only the PowerBI side of things are fully supported but I have very good hopes that pipelines and notebooks will be supported as well.
Supporting pie charts are fightin’ words here. Nonetheless, read on to see how deployment pipelines work in Microsoft Fabric.
2 CommentsKevin Chant creates a database project:
In this post I want to cover how you can share a Microsoft Fabric Data Warehouse Database Project with the new target platform.
Which is now possible thanks to the latest Azure Data Studio Insiders update. You can view the ‘Add projects support for Fabric DW‘ pull request in the public azuredatastudio GitHub repository.
Kevin takes us through creating the database project in Azure Data Studio and then using Azure DevOps or Azure Data Studio to deploy it back out.
Comments closed