New Microsoft Certified: SQL AI Developer Associate Certification
AI is transforming how data-driven applications are built, and SQL professionals are at the center of this new era. Whether you're a database developer, administrator, analyst, or architect, your SQL expertise is more critical than ever. With the rise of AI, quality data and secure, optimized queries are foundational to building intelligent, scalable solutions. We’re introducing the Microsoft Certified: SQL AI Developer Associate Certification to help you validate your ability to integrate AI capabilities directly into SQL-based solutions without needing to move your data, learn entirely new platforms, or move away from the T-SQL skills you already use every day. To earn this new Certification, you need to pass Exam DP‑800: Developing AI‑Enabled Database Solutions, currently in beta. Is this the right Certification for you? The Microsoft Certified: SQL AI Developer Associate Certification is designed for SQL professionals who build and maintain SQL-based applications and who want to integrate AI capabilities directly into their data solutions. As you collaborate closely with application developers, database administrators, architects, AI engineers, and DevSecOps teams, skills validated by this certification support the delivery of scalable, secure, and high-performance AI‑enabled applications. This Certification demonstrates your ability to: Design and build database solutions using structured and semi-structured data. Use AI-assisted tools to accelerate SQL development and database management. Secure, optimize, and deploy enterprise-grade SQL solutions. Implement vectors, embeddings, and retrieval-augmented generation (RAG) patterns within SQL database architectures. As a candidate for this Certification, you should have experience writing T-SQL, developing databases on Microsoft SQL platforms, and working with continuous integration and continuous deployment (CI/CD) workflows in GitHub. We also recommend familiarity with AI concepts, such as embeddings, vectors, and models, in addition to AI-assisted development tools. Skills measured by the exam include: Applying advanced T-SQL techniques for AI-ready database solutions. Building vector and semantic search experiences directly in SQL. Implementing RAG workflows to ground large language model (LLM) outputs and reduce hallucinations. Integrating LLMs into SQL-based applications without migrating data. Designing secure, compliant, and scalable AI-enabled data solutions. Exposing SQL data through APIs by using Data API builder. Building and monitoring data APIs by using Data API builder and event-driven change patterns. These capabilities power use cases like semantic and hybrid search, chatbots, personalized recommendations, fraud detection, and predictive analytics. Ready to prove your skills? The first 300 candidates can save 80% Take advantage of the discounted beta exam offer. The first 300 people who take Exam DP-800 (beta) on or before April 3, 2026, can get 80% off. To receive the discount, when you register for the exam and are prompted for payment, use code DP800Belzoni. This is not a private access code. The seats are offered on a first-come, first-served basis. As noted, you must take the exam on or before April 3, 2026. Please note that this discount is not available in Turkey, Pakistan, India, or China. How to prepare Get ready to take Exam DP-800 (beta): Review the Exam DP-800 (beta) exam page for details. The Exam DP-800 study guide explores key topics covered in the exam. Work through the self-paced skilling Plan on Microsoft Learn: Become a SQL AI Developer: Prepare for Certification Exam DP-800. Connect with Microsoft Training Services Partners in your area for in-person offerings. Need other preparation ideas? Check out Just How Does One Prepare for Beta Exams? Ready to get started? Remember, the number of spots is limited to the first 300 candidates taking Exam DP-800 (beta) on or before April 3, 2026. Beta exam rescoring begins when the exam goes live, with final results released approximately 10 days later. For more details, read Creating high-quality exams: The path from beta to live. Stay tuned for general availability of this Certification in May 2026. Additional information You can take Certification exams online, from your home or office. Learn what to expect in Online proctored exams: What to expect and how to prepare. For more cloud and AI Certification updates, read our recent blog post, The AI job boom is here. Are you ready to showcase your skills? Get involved: Help shape future Microsoft Credentials. Join our Microsoft Worldwide Learning SME Group for Credentials on LinkedIn for beta exam alerts and opportunities to help shape future Microsoft learning and assessments. Explore Microsoft Credentials on AI Skills Navigator.Modern SQL Server Features That Make Life Better
🚀 Excited to share an upcoming session you won’t want to miss! 📌 Modern SQL Server Features That Make Life Better As data platforms evolve, staying ahead of the curve is essential for every database developer and administrator. This session dives into the latest advancements in SQL Server, including powerful capabilities introduced in SQL Server 2022 that transform the way we manage, optimise, and troubleshoot data workloads. 🔍 What you’ll learn: • How Intelligent Query Processing and Query Store simplify performance tuning and troubleshooting • The impact of Memory Grant Feedback and DOP Feedback on real-world workload performance • New T-SQL enhancements that help developers write cleaner, more efficient code • How temporal tables enable trending over time, point-in-time recovery, and fixing accidental data changes • Key modern features that make database operations more scalable, predictable, and efficient Whether you're a DBA or a developer, this session will equip you with practical insights to make your day-to-day work easier — and your SQL Server environments smarter. 💡Join us and elevate your SQL Server expertise! 🗓️ Date: 22 November 2025 ⏰ Time: 18:00 (CET) 🎙️ Speaker: Lee Markum 📌 Topic: Modern SQL Server Features That Make Life BetterLeveraging DotNet for SQL Builds via YAML
Ready to streamline your SQL database deployments? In the latest installment of our SQL Databases series, we dive into building a .dacpac file using Azure DevOps Pipelines. We'll walk you through automating the process of converting a .sqlproj into a reusable .dacpac, perfect for deploying across any Azure environment. This post not only covers the essential build steps but also highlights the importance of version control and how to optimize your pipeline for multi-stage deployments. Don’t miss out—learn how to create a robust deployment strategy and prepare for the next step: deploying your .dacpac to SQL Server!Technical Walkthrough: Deploying a SQL DB like it's Terraform
Introduction This post will be a union of multiple topics. It is part of the SQL CI/CD series and as such will build upon Deploying .dacpacs to Multiple Environments via ADO Pipelines | Microsoft Community Hub and Managed SQL Deployments Like Terraform | Microsoft Community Hub while also crossing over with the YAML Pipeline series This is an advanced topic in regard to both Azure DevOps YAML and SQL CI/CD. If both of these concepts are newer to you, please refer to the links above as this is not designed to be a beginner's approach in either one of these domains. Assumptions To get the most out of this and follow along we are going to assume that you are 1.) On board with templating your Azure DevOps YAML Pipelines. By doing this we will see the benefit of quickly onboarding new pipelines, standardizing our deployment steps, and increasing our security. We also are going to assume you are on board with Managed SQL Deployments Like Terraform | Microsoft Community Hub for deploying your SQL Projects. By adopting this we can increase our data security, confidence in source control, and speed our time to deployment. For this post we will continue to leverage the example cicd-adventureWorks repository for the source of our SQL Project and where the pipeline definition will live. Road mapping the Templates Just like my other YAML posts let's outline the pieces required in this stage and we will then break down each job Build Stage Build .dacpac job run `dotnet build` and pass in appropriate arguments execute a Deploy Report from the dacpac produced by the build and the target environment copy the Deploy Report to the build output directory publish the pipeline artifact Deploy Stage Deploy .dacpac job run Deploy Report from dacpac artifact (optional) deploy dacpac, including pre/postscripts Build Stage For the purposes of this stage, we should think of building our .dacpac similar to a terraform or single page application build. What I am referring to is we will produce an artifact per environment, and this will be generated from the same codebase. Additionally, we will run a 'plan' which will be the proposed result of deploying our dacpac file. Build Job We will have one instance of the build job for each environment. Each instance will produce a different artifact as they will be passing different build configurations which in turn will result in a different .dacpac per environment. If you are familiar with YAML templating, then feel free to jump to the finish job template. One of the key differences with this job structure, as opposed to the one outlined in Deploying .dacpacs to Multiple Environments via ADO Pipelines is the need for a Deploy Report. This is the key to unlocking the CI/CD approach which aligns with Terraform. This Deploy Report detects our changes on build, similar to running a terraform plan. Creating a Deploy Report is achieved by setting the DeployAction attribute in the SQLAzureDacpacDeployment@1 action to 'DeployReport' Now there is one minor "bug" in the Microsoft SQLAzureDacpacDeployment task, which I have raised with the ADO task. It appears the output path for the Deploy Report as well as the Drift Report are hardcoded to the same location. To get around this I had to find out where the Deploy Report was being published and, for our purposes, have a task to copy the Deploy Report to the same location as the .dacpac and then publish them both as a single folder. Here is the code for the for a single environment to build the associated .dacpac and produce the Deploy Repo stages: - stage: adventureworksentra_build variables: - name: solutionPath value: $(Build.SourcesDirectory)// jobs: - job: build_publish_sql_sqlmoveme_dev_dev steps: - task: UseDotNet@2 displayName: Use .NET SDK vlatest inputs: packageType: 'sdk' version: '' includePreviewVersions: true - task: NuGetAuthenticate@1 displayName: 'NuGet Authenticate' - task: DotNetCoreCLI@2 displayName: dotnet build inputs: command: build projects: $(Build.SourcesDirectory)/src/sqlmoveme/*.sqlproj arguments: --configuration dev /p:NetCoreBuild=true /p:DacVersion=1.0.1 - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\s/src/sqlmoveme/bin/dev/sqlmoveme.dacpac AdditionalArguments: '' DeleteFirewallRule: True - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: $(Build.SourcesDirectory)/src/sqlmoveme/bin/dev/cus - task: PublishPipelineArtifact@1 displayName: 'Publish Pipeline Artifact sqlmoveme_dev_dev ' inputs: targetPath: $(Build.SourcesDirectory)/src/sqlmoveme/bin/dev artifact: sqlmoveme_dev_dev properties: '' The end result will be similar to: (I have two environments in the screenshot below) One can see I have configured this to run a Deploy Report across each regional instance, thus the `cus` folder, of a SQL DB I do this is to identify and catch any potential schema and data issues. The Deploy Reports are the keys to tie this to the thought of deploying and managing SQL Databases like Terraform. These reports will execute when a pull request is created as part of the Build and again at Deployment to ensure changes from PR to deployment that may have occurred. For the purposes of this blog here is a deployment report indicating a schema change: This is an important artifact for organizations whose auditing policy requires documentation around deployments. This information is also available in the ADO job logs: This experience should feel similar to Terraform CI/CD...THAT'S A GOOD THING! It means we are working on developing and refining practices and principals across our tech stacks when it comes to SDLC. If this feels new to you then please read Terraform, CI/CD, Azure DevOps, and YAML Templates - John Folberth Deploy Stage We will have a deploy stage for each environment and within that stage will be a job for each region and/or database we are deploying our dacpac to. This job can be a template because, in theory, our deploying process across environments is identical. We will run a deployment report and deploy the .dacpac which was built for the specific environment and will include any and all associated pre/post scripts. Again this process has already been walked through in Deploying .dacpacs to Multiple Environments via ADO Pipelines | Microsoft Community Hub Deploy Job The deploy job will take what we built in the deployment process in Deploying .dacpacs to Multiple Environments via ADO Pipelines | Microsoft Community Hub and we will add a perquisite job to create a second Deployment Report. This process is to ensure we are aware of any changes in the deployed SQL Database that may have occurred after the original dacpac and Deployment Report were created at the time of the Pull Request. By doing this we now have a tight log identifying any changes that were being made right before we deployed the code. Next, we need to make a few changes to override the default arguments of the .dacpac publish command in order to automatically deploy changes that may result in data loss. Here is a complete list of all the available properties SqlPackage Publish - SQL Server | Microsoft Learn. The ones we are most interested in are DropObjectsNotInSource and BlockOnPossibleDataLoss. DropObjectsNotInSource is defined as: Specifies whether objects that do not exist in the database snapshot (.dacpac) file will be dropped from the target database when you publish to a database. This value takes precedence over DropExtendedProperties. This is important as it will drop and delete objects that are not defined in our source code. As I've written about previously this will drop all those instances of "Shadow Data" or copies of tables we were storing. This value, by default, is set to false as a safeguard from a destructive data action. Our intention though is to ensure our deployed database objects match our definitions in source control, as such we want to enable this. BlockOnPossibleDataLoss is defined as: Specifies that the operation will be terminated during the schema validation step if the resulting schema changes could incur a loss of data, including due to data precision reduction or a data type change that requires a cast operation. The default (True) value causes the operation to terminate regardless if the target database contains data. An execution with a False value for BlockOnPossibleDataLoss can still fail during deployment plan execution if data is present on the target that cannot be converted to the new column type. This is another safeguard that has been put in place to ensure data isn't lost in the situation of type conversion or schema changes such as dropping a column. We want this set to `true` so that our deployment will actually deploy in an automated fashion. If this is set to `false` and we are wanting to update schemas/columns then we would be creating an anti-pattern of a manual deployment to accommodate. When possible, we want to automate our deployments and in this specific case we have already taken the steps of mitigating unintentional data loss through our implementation of a Deploy Report. Again, we should have confidence in our deployment and if we have this then we should be able to automate it. Here is that same deployment process, including now the Deploy Report steps: - stage: adventureworksentra_dev_cus_dacpac_deploy jobs: - deployment: adventureworksentra_app_dev_cus environment: name: dev dependsOn: [] strategy: runOnce: deploy: steps: - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_dev_dev\**\*.dacpac AdditionalArguments: '' DeleteFirewallRule: False - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: postDeploy/sql-adventureworksentra-dev-cus.database.windows.net/sqlmoveme - task: SqlAzureDacpacDeployment@1 displayName: Publish sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: Publish azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_dev_dev\**\*.dacpac AdditionalArguments: /p:DropObjectsNotInSource=true /p:BlockOnPossibleDataLoss=false DeleteFirewallRule: True Putting it Together Let's put together all these pieces. This example will show an expanded pipeline that has the following stages and jobs Build a stage Build Dev job Build Tst job Deploy Dev stage Deploy Dev Job Deploy tst stage Deploy tst Job And here is the code: resources: repositories: - repository: templates type: github name: JFolberth/TheYAMLPipelineOne endpoint: JFolberth trigger: branches: include: - none pool: vmImage: 'windows-latest' parameters: - name: projectNamesConfigurations type: object default: - projectName: 'sqlmoveme' environmentName: 'dev' regionAbrvs: - 'cus' projectExtension: '.sqlproj' buildArguments: '/p:NetCoreBuild=true /p:DacVersion=1.0.1' sqlServerName: 'adventureworksentra' sqlDatabaseName: 'moveme' resourceGroupName: adventureworksentra ipDetectionMethod: 'AutoDetect' deployType: 'DacpacTask' authenticationType: 'servicePrincipal' buildConfiguration: 'dev' dacpacAdditionalArguments: '/p:DropObjectsNotInSource=true /p:BlockOnPossibleDataLoss=false' - projectName: 'sqlmoveme' environmentName: 'tst' regionAbrvs: - 'cus' projectExtension: '.sqlproj' buildArguments: '/p:NetCoreBuild=true /p:DacVersion=1.0' sqlServerName: 'adventureworksentra' sqlDatabaseName: 'moveme' resourceGroupName: adventureworksentra ipDetectionMethod: 'AutoDetect' deployType: 'DacpacTask' authenticationType: 'servicePrincipal' buildConfiguration: 'tst' dacpacAdditionalArguments: '/p:DropObjectsNotInSource=true /p:BlockOnPossibleDataLoss=false' - name: serviceName type: string default: 'adventureworksentra' stages: - stage: adventureworksentra_build variables: - name: solutionPath value: $(Build.SourcesDirectory)// jobs: - job: build_publish_sql_sqlmoveme_dev_dev steps: - task: UseDotNet@2 displayName: Use .NET SDK vlatest inputs: packageType: 'sdk' version: '' includePreviewVersions: true - task: NuGetAuthenticate@1 displayName: 'NuGet Authenticate' - task: DotNetCoreCLI@2 displayName: dotnet build inputs: command: build projects: $(Build.SourcesDirectory)/src/sqlmoveme/*.sqlproj arguments: --configuration dev /p:NetCoreBuild=true /p:DacVersion=1.0.1 - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\s/src/sqlmoveme/bin/dev/sqlmoveme.dacpac AdditionalArguments: '' DeleteFirewallRule: True - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: $(Build.SourcesDirectory)/src/sqlmoveme/bin/dev/cus - task: PublishPipelineArtifact@1 displayName: 'Publish Pipeline Artifact sqlmoveme_dev_dev ' inputs: targetPath: $(Build.SourcesDirectory)/src/sqlmoveme/bin/dev artifact: sqlmoveme_dev_dev properties: '' - job: build_publish_sql_sqlmoveme_tst_tst steps: - task: UseDotNet@2 displayName: Use .NET SDK vlatest inputs: packageType: 'sdk' version: '' includePreviewVersions: true - task: NuGetAuthenticate@1 displayName: 'NuGet Authenticate' - task: DotNetCoreCLI@2 displayName: dotnet build inputs: command: build projects: $(Build.SourcesDirectory)/src/sqlmoveme/*.sqlproj arguments: --configuration tst /p:NetCoreBuild=true /p:DacVersion=1.0 - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-tst-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureTstServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-tst-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\s/src/sqlmoveme/bin/tst/sqlmoveme.dacpac AdditionalArguments: '' DeleteFirewallRule: True - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: $(Build.SourcesDirectory)/src/sqlmoveme/bin/tst/cus - task: PublishPipelineArtifact@1 displayName: 'Publish Pipeline Artifact sqlmoveme_tst_tst ' inputs: targetPath: $(Build.SourcesDirectory)/src/sqlmoveme/bin/tst artifact: sqlmoveme_tst_tst properties: '' - stage: adventureworksentra_dev_cus_dacpac_deploy jobs: - deployment: adventureworksentra_app_dev_cus environment: name: dev dependsOn: [] strategy: runOnce: deploy: steps: - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_dev_dev\**\*.dacpac AdditionalArguments: '' DeleteFirewallRule: False - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: postDeploy/sql-adventureworksentra-dev-cus.database.windows.net/sqlmoveme - task: SqlAzureDacpacDeployment@1 displayName: Publish sqlmoveme on sql-adventureworksentra-dev-cus.database.windows.net inputs: DeploymentAction: Publish azureSubscription: AzureDevServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-dev-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_dev_dev\**\*.dacpac AdditionalArguments: /p:DropObjectsNotInSource=true /p:BlockOnPossibleDataLoss=false DeleteFirewallRule: True - stage: adventureworksentra_tst_cus_dacpac_deploy jobs: - deployment: adventureworksentra_app_tst_cus environment: name: tst dependsOn: [] strategy: runOnce: deploy: steps: - task: SqlAzureDacpacDeployment@1 displayName: DeployReport sqlmoveme on sql-adventureworksentra-tst-cus.database.windows.net inputs: DeploymentAction: DeployReport azureSubscription: AzureTstServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-tst-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_tst_tst\**\*.dacpac AdditionalArguments: '' DeleteFirewallRule: False - task: CopyFiles@2 inputs: SourceFolder: GeneratedOutputFiles Contents: '**' TargetFolder: postDeploy/sql-adventureworksentra-tst-cus.database.windows.net/sqlmoveme - task: SqlAzureDacpacDeployment@1 displayName: Publish sqlmoveme on sql-adventureworksentra-tst-cus.database.windows.net inputs: DeploymentAction: Publish azureSubscription: AzureTstServiceConnection AuthenticationType: servicePrincipal ServerName: sql-adventureworksentra-tst-cus.database.windows.net DatabaseName: sqlmoveme deployType: DacpacTask DacpacFile: $(Agent.BuildDirectory)\sqlmoveme_tst_tst\**\*.dacpac AdditionalArguments: /p:DropObjectsNotInSource=true /p:BlockOnPossibleDataLoss=false DeleteFirewallRule: True In ADO it will look like: We can see the important Deploy Report being created and can confirm that there are Deploy Reports for each environment/region combination: Conclusion With the inclusion of deploy reports we now have the ability to create Azure SQL Deployments that adhere to modern DevOps approaches. We can ensure our environments will be in sync with how we have defined them in source control. By doing this we achieve a higher level of security, confidence in our code, and reduction in shadow data. To learn more on these approaches with SQL Deployments be sure to check out my other blog articles on the topic "SQL Database Series" in "Healthcare and Life Sciences Blog" | Microsoft Community Hub and be sure to follow me on LinkedInManaged SQL Deployments Like Terraform
Introduction This is the next post in our series on CI/CD for SQL projects. In this post we will challenge some long held beliefs on how we should manage SQL Deployments. Traditionally we've always had this notion that we should never drop data in any environment. Deployments should almost extensively be done via SQL scripts and manually ran to ensure completion and to prevent any type of data loss. We will challenge this and propose a solution that falls more in line with other modern DevOps tooling and practices. If this sounds appealing to you then let's dive into it. Why We've always approached the data behind our applications as the differentiating factor when it comes to Intellectual Property (IP). No one wants to hear the words that we've lost data or that the data is unrecoverable. Let me be clear and throw a disclaimer on what I am going to propose, this is not a substitute for proper data management techniques to prevent data loss. Rather we are going to look at a way to thread the needle on keeping the data that we need while removing the data that we don't. Shadow Data We've all heard about "shadow IT", well what about "shadow data"? I think every developer has been there. For example, taking a backup of a table/database to ensure we don't inadvertently drop it during a deployment. Heck sometimes we may even go a step further and backup this up into a lower environment. The caveat is that we very rarely ever go back and clean up that backup. We've effectively created a snapshot of data which we kept for our own comfort. This copy is now in an ungoverned, unmanaged, and potentially in an insecure state. This issue then gets compounded if we have automated backups or restore to QA operations. Now we keep amplifying and spreading our shadow data. Shouldn't we focus on improving the Software Delivery Lifecycle (SDLC), ensuring confidence in our data deployments? Let's take it a step further and shouldn't we invest in our data protection practice? Why should we be doing this when we have technology that backs up our SQL schema and databases? Another consideration, what about those quick "hot fixes" that we applied in production. The ones where we changed a varchar() column length to accommodate the size of a field in production. I am not advocating for making these changes in production...but when your CIO or VP is escalating since this is holding up your businesses Data Warehouse and you so happen to have the SQL admin login credentials...stuff happens. Wouldn't it be nice if SQL had a way to report back that this change needs to be accommodated for in the source schema? Again, the answer is in our SDLC process. So, where is the book of record for our SQL schemas? Well, if this is your first read in this series or if you are unfamiliar with source control I'd encourage you to read Leveraging DotNet for SQL Builds via YAML | Microsoft Community Hub where I talk about the importance of placing your database projects under source control. The TL/DR...your database schema definitions should be defined under source control, ideally as a .sqlproj. Where Terraform Comes In At this point I've already pointed out a few instances on how our production database instance can differ from what we have defined in our source project. This certainly isn't anything new in software development. So how does other software development tooling and technologies account for this? Generally, application code simply gets overwritten, and we have backup versions either via release branches, git tags, or other artifacts. Cloud infrastructure can be defined as Infrastructure as Code (IaC) and as such still follow something similar to our application code workflow. There are two main flavors of IaC for Azure: Bicep/ARM and Terraform. Bicep/ARM adheres to an incremental deployment, which has its pros and cons. The quick version is Azure Resource Manager (ARM) deployments will not delete resources that are not defined in its template. Part of this has led to Azure Deployment Stacks which can help enforce resource deletion when it's been removed from a template. If interested in understanding a Terraform workflow I will point you to one of my other posts on the topic. At a high level Terraform evaluates your IaC definition and determines what properties need to be updated, and more importantly, what resources need to be removed. Now how does Terraform do this and more importantly, how can we tell what properties will be updated and/or removed? Terraform has a concept known as a plan. This plan will run your deployment against what is known as the state file, in Bicep/ARM this is the Deployment Stack, and produce a summary of changes that will occur. This includes new resources to be created, modification of existing resources, and deletion of resources previously deployed to the same state file. Typically, I recommend running a Terraform plan across all environments at CI. This ensures one can evaluate changes being proposed across all potential environments and summarize these changes at the time of the Pull Request (PR). I then advise re-executing this plan prior to deployment as a way to confirm/re-evaluate if anything has been updated since the original plan ran. Some will argue the previous plan can be "approved" to deploy to the next environment; however, there is little overhead in running a second plan and I prefer this option. Here's the thing....SQL actually has this same functionality. Deploy Reports Via SqlPackage there is additional functionality we can leverage with our .dacpacs. We are going to dive a little more on Deploy Reports. If you have followed this series, you may know we use the SqlPackage Publish command wrapped behind the SqlAzureDacpacDeployment@1 task. More information on this can be found at Deploying .dapacs to Azure SQL via Azure DevOps Pipelines | Microsoft Community Hub . So, what is a Deploy Report? A Deploy Report is the XML representation of the changes your .dacpac will make to a database. Here is an example of one denoting that there is a risk of potential data loss: This report is the key to our whole argument for modeling a SQL Continuous Integration/Continous Delivery after one that Terraform uses. We already will have a separate .dacpac file, built from the same .sqlproj, for each environment when leveraging pre/post scripts as we saw in Deploying .dacpacs to Multiple Environments via ADO Pipelines | Microsoft Community Hub. So now we need to take each one of those and run a Deploy Report against the appropriate target. This is the same as effectively running a `tf plan` with a different variable file against each environment to determine what actions a Terraform `apply` will execute. These Deploy Reports are then what we will include in our PR approval to validate and approve any changes we will make to our SQL database. Dropping What's Not in Source Control This is the controversial part and the biggest sell in our adoption of a Terraform like approach to SQL deployments. It has long been considered a best practice to have whatever is deployed match what is under source control. This provides for a consistent experience when developing and then deploying across multiple environments. Within IaC, we have our cloud infrastructure defined in source control and deployed across environments. Typically, it is seen as a good practice to delete resources which have been removed from source control. This helps simplify the environment, reduces cost, and reduces potential security surface areas. So why not the same for databases? Typically, it is due to us having the fear of losing data. To prevent this, we should have proper data protection and recovery processes in place. Again, I am not addressing that aspect. If we have those accounted for, then by all means, our source control version of our databases should match our deployed environments. What about security and indexing? Again, this can be accounted for in Deploying .dacpacs to Multiple Environments via ADO Pipelines | Microsoft Community Hub. Where we have two different post deployment security scripts, and these scripts are under source control! How can we see if data loss will occur? Refer back to the Deploy Reports for this! There potentially is some natural hesitation as the default method for deploying a .dacpac has safeguards to prevent deployments in the event of potential data loss. This is not a bad thing as it prevents a destructive activity from automatically occurring; however, we by no means need to accept the default behavior. We will need to refer to SqlPackage Publish - SQL Server | Microsoft Learn. From this list we will be able to identify and explicitly set the value for various parameters. These will enable our package to deploy even in the event of potential data loss. Conclusion This post hopefully challenges the mindset we have when it comes to database deployments. By taking an approach that more closely relates to modern DevOps practices, we can gain confidence that our source control and database match, increased reliability and speed with our deployments, and we are closing potential security gaps in our database deployment lifecycle. This content was not designed to be technical. In our next post we will demo, provide examples, and talk through how to leverage YAML Pipelines to accomplish what we have outlined here. Be sure to follow me on LinkedIn for the latest publications. For those who are technically sound and want to skip ahead feel free to check out my code on my GitHub : https://github.com/JFolberth/cicd-adventureWorks and https://github.com/JFolberth/TheYAMLPipelineOneDeploying .dapacs to Azure SQL via Azure DevOps Pipelines
🚀 Ready to deploy your .dacpac to Azure using Azure DevOps? In this post from my SQL Database series, I’ll walk you through the next step—deploying your .dacpac file via an Azure DevOps YAML Pipeline. If you've already built the .dacpac, you're almost there! I’ll cover how to set up authentication, configure the pipeline, and deploy your database securely using Microsoft Entra Authentication. Full YAML code and detailed steps are available in my GitHub repository. Next, we'll explore deploying to multiple environments. Stay tuned!How to Query Spark Tables from Serverless SQL Pools in Azure Synapse
Introduction Say goodbye to constantly running Spark clusters! With the shared metadata functionality, you can shut down your Spark pools while still be able to query your Spark external tables using Serverless SQL Pool. In this blog we dive into, how Serverless SQL Pool streamlines your data workflow by automatically synchronizing metadata from your Spark pools. Shared Metadata functionality Azure Synapse Analytics allows the different workspace computational engines to share databases and tables between its Apache Spark pools and serverless SQL pool. When we create tables in Apache Spark Pool, whether managed or external, the Serverless SQL pool automatically synchronizes its metadata. This metadata synchronization automatically creates a corresponding external table in a serverless SQL pool database. Then after a short delay, we can see the table in our Serverless SQL pool. Creating a managed table in Spark and querying from Serverless SQL Pool Now we can shut down our Spark pools and still be able to query Spark external tables from Serverless SQL Pool. NOTE: Azure Synapse currently only shares managed and external Spark tables that store their data in Parquet, DELTA, or CSV format. Tables backed by other formats are not automatically synced. You may be able to sync such tables explicitly yourself as an external table in your own SQL database if the SQL engine supports the table's underlying format. Also, External tables created in Spark are not available in dedicated SQL pool databases. Why we get an error if you use dbo schema in Spark pool or if you don’t use dbo schema in Serverless SQL pool? The dbo schema (short for “database owner”) is the default schema in SQL Server and Azure Synapse SQL pools. Spark pool only supports user-defined schemas. Means, it does not recognize dbo as a valid schema name. While in Serverless SQL Pool, all the tables belong to the dbo schema, regardless of their original schema in Spark pool or other sources.Deploying .dacpacs to Multiple Environments via ADO Pipelines
Ready to crank up your SQL deployment skills? Our latest post takes you through the next step in deploying .dacpac files across multiple environments using Azure DevOps Pipelines. We'll dive deep into creating tailored pre/post-deployment scripts for dev and test environments, configuring security with Entra groups, and using build configurations to handle different environments. If you want to elevate your database deployment process, this is the post for you. Full code is available on GitHub, so follow along and take your deployments to the next level!
