Copa: An Image Vulnerability Patching Tool
Securing container images is paramount, especially with the widespread adoption of containerization technologies like Docker and Kubernetes. Microsoft has recognized the need for robust image security solutions and has introduced Copa, an open-source tool designed to keep container images secure and address vulnerabilities swiftly. Learn about Copa in this blog.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 LinkedInGetting secrets from Key Vault in YAML pipeline
If you have ever created an Azure App Service or Azure Function App that uses app settings, then you have dealt with the problem of how you are going to get those settings secure and updated correctly in each environment. You need a secure location to store this information and then be able to access it during your deployment process. Azure Key Vault and using the Azure Key Vault task inside a deployment pipeline in Azure DevOps can solve this problem for you. If you prefer video, then have a look at this as it will walk you through the steps of getting this setup.
Utilizing Azure Key vault with Private link in DevOps
Azure Key Vault is a cloud service that provides secure storage and access to secrets such as API keys, passwords, certificates, or cryptographic keys. To enhance security and disable public access, Azure Key Vault can be integrated with Private Endpoint powered by Azure Private Link. This private endpoint uses a private IP address from your VNet and brings the service into your VNet, effectively eliminating exposure from the public Internet by traversing traffic between your virtual network and the service over the Microsoft backbone network.Part 6: Introducing Deployment Stacks to Azure Data Factory
Dive into Part 6 as we explore the integration of Azure Deployment Stacks into Azure DevOps Pipelines. Discover how to remove unused resources from your Azure Data Factory as part of your CI/CD Process!. Stay tuned for a deep dive into deployment stacks and their impact on Data Factory efficiency.The History of Microsoft Azure
Learn about the history of Microsoft Azure a leading giant in the cloud service industry which offers rich services on platform as a service (PaaS), software as a service (Saas) and infrastructure as a service (IaaS). This will take us back to the moments on how this powerful and sophisticated service began, revealing the resilience and vision of the Microsoft company as a brand, the present stages and how to partake of the cake Microsoft has provided for businesses and developers.Setup Azure DevOps Self Hosted Agent On-Premise & Troubleshooting Guidelines - Part 2
Azure Pipelines provides a powerful and flexible platform for continuous integration and deployment. While Microsoft offers hosted agents for executing pipeline tasks, there are scenarios where a self-hosted agent becomes necessary. In this blog post, we will walk you through the process of creating a self-hosted agent for Azure Pipelines, empowering you with the ability to run your pipelines on your own infrastructure. Benefits of self-hosted agents in Azure DevOps: Increased flexibility: Self-hosted agents allow you to run builds, tests, and deployments on platforms that are not supported by Azure Pipelines. Additionally, you can customize the environments to suit your specific needs and take advantage of the latest hardware capabilities. Greater control over the environment: Self-hosted agents give you full control over the environment, allowing you to access and install anything you need for your builds and tests. Seamless integration: With self-hosted agents, you can easily integrate your existing DevOps tools, such as source control, build systems, and test frameworks, into Azure Pipelines for a seamless DevOps experience. Cost savings: Self-hosted agents can help you save money on build-related costs, as you can use existing hardware to run your builds. You can also save costs by managing your own agents and using lower-cost cloud providers. Common Challenges: Setup: Setting up and configuring a self-hosted agent can be a time-consuming and complex process, as you have to install all necessary dependencies and configure the environment to suit your needs. Maintenance: With self-hosted agents, you are responsible for all maintenance activities, such as patching, updates, and security. Security: As you are managing your own environment, you need to ensure that all access is secure and comply with the required security standards. Scalability: Self-hosted agents can be difficult to scale, as you have to manually configure and manage additional agents as your needs grow.Azure OpenAI GPT model to review Pull Requests for Azure DevOps
In recent months, the use of Generative Pre-trained Transformer (GPT) models for natural language processing (NLP) has gained significant traction. GPT models, which are based on the Transformer architecture, can generate text from arbitrary sources of input data and can be trained to identify errors and detect anomalies in text. As such, GPT models are increasingly being used for a variety of applications, ranging from natural language understanding to text summarization and question-answering. In the software development world, developers use pull requests to submit proposed changes to a codebase. However, reviews by other developers can sometimes take a long time and not accurate, and in some cases, these reviews can introduce new bugs and issues. In order to reduce this risk, During my research I found the integration of GPT models is possible and we can add Azure OpenAI service as pull request reviewers for Azure Pipelines service. The GPT models are trained on developer codebases and are able to detect potential coding issues such as typos, syntax errors, style inconsistencies and code smells. In addition, they can also assess code structure and suggest improvements to the overall code quality. Once the GPT models have been trained, they can be integrated into the Azure Pipelines service so that they can automatically review pull requests and provide feedback. This helps to reduce the time taken for code reviews, as well as reduce the likelihood of introducing bugs and issues.Vulnerability Assessment on Azure Container Registry with Microsoft Defender and Docker Hub
Azure Container Registry: Microsoft Defender for Containers and Docker Scout. Rather than comparing these tools, I want to highlight how they complement each other to enhance container security on ACR. Containers revolutionize the way applications are deployed, providing a consistent and efficient way to package code and dependencies. However, with this convenience comes the critical need for robust security measures. In this post, we'll explore why container security is essential, focusing on preventing malicious code, avoiding vulnerabilities, and managing configuration and deployment risks.
