To deploy Apache Ignite or In-Memory Computing Platforms like Hazelcast in a Docker image on Kubernetes in the Azure public cloud and then redirect these services to other services in private VMs deployed using Docker Compose, you can follow these general steps:

Step 1: Docker Image Creation

• Create Docker images for Apache Ignite or In-Memory Computing Platforms like Hazelcast. You can use Dockerfiles to define the environment and dependencies required for your specific application.

Step 2: Azure Kubernetes Service (AKS) Setup

• up an Azure Kubernetes Service (AKS) cluster where you deploy your Docker images. You can use Azure Portal, Azure CLI, or templates to create and manage your Kubernetes cluster.

Step 3 Deployment to Kubernetes

• Deploy the created Docker images to the Kubernetes cluster on Azure. You can use Kubernetes manifests (YAML files) to define deployments, services, and any required configurations.

Step 4: Networking Setup

• Configure networking in the AKS cluster to enable communication between the services running in the Kubernetes cluster and the services running in the private VMs. This could involve using Azure Virtual Network (VNet) pe or VPN Gateway for secure communication.

Step 5: Docker Compose for Private VMs

• Set up the private VMs using Docker Compose to deploy other services. Define the services, networks, and volumes required for your application in the Docker Compose file.

Step 6: Networking between Kubernetes and Private VMs

• Configure networking between the Kubernetes cluster and the private VMs. This may involve setting up proper DNS resolution, private IP communication, or using an Ingress controller to expose the services running in the Kubernetes cluster the private VMs.

By following these steps, you can deploy Ignite or In-Memory Computing Platforms on Kubernetes in Azure and connect them to your services running in private VMs deployed using Docker Compose. Always ensure to consider security best practices and network isolation when setting up communication between resources in the public cloud and VMs.

To integrate Trivy, a comprehensive vulnerability scanner for containers, with your Azure build pipeline, you can follow these steps:

Step 1: Install Trivy

• Ensure that Trivy is installed on the machine where your Azure build pipeline agent will execute the builds. You can either install Trivy directly on the agent machine or use containerization to run Trivy as a Docker container within your pipeline.

Step 2: Configure Trivy Scan

• In your Azure build pipeline, add a step to run the Trivy scan. This can be achieved by using a script or running Trivy directly as a command-line tool. Make sure to provide the appropriate flags and arguments to scan the container images used in your pipeline.

Step 3: Generate Trivy Report

• After running the Trivy scan, capture the results in a report format. Trivy provides various output options, including JSON, SARIF, and others. You can generate the report in a format that integrates well with Azure DevOps, such as JSON.

Step 4: Publish Trivy Results

• Publish the Trivy scan results within your Azure build pipeline. Most often, this involves using a built-in task or script to publish the Trivy report to Azure DevOps so that the vulnerability findings can be visualized within the build pipeline.

Step 5: Define Policy Actions

• Based on the Trivy scan results, define policy actions within your Azure build pipeline. For example, you can introduce gates or checks that prevent deployment or promotion of container images if they contain critical vulnerabilities identified by Trivy.

Step 6: Continuous Integration

• Integrate the Trivy scan into your continuous integration process to ensure that all container images built as part of your pipeline are scanned for vulnerabilities before being deployed to your infrastructure.

By following these steps, you can effectively integrate Trivy into your Azure build pipeline, enabling you to proactively identify and address vulnerabilities within your container images during the build and deployment process.

Certainly! Here's an example of how you can configure a Trivy scan within an Azure DevOps (Azure Pipelines) pipeline:

# azure-pipelines.yml

trigger:
- main

pool:
  vmImage:ubuntu-latest'

steps:
- task: UsePythonVersion@0
  inputs:
    versionSpec '3.x'
    addToPath: true

- script: |
    # Install Trivy
    wget https://github.com/aqucurity/trivy/releases/download/v0.18.3/trivy_0.18.3_Linux-64bit.tar.gz    tar zxvf trivy_0.18.3_Linux-64bit.tar.gz
    export PATH=$:$(System.DefaultWorkingDirectory)/trivy

  displayName 'Install Trivy'

- script: |
    # Run Trivy scan on a Docker image
    trivy --exit-code 1 --severityCRITICAL my-container-image:latest

  displayName: 'Run Trivy Scan'

- task: PublishBuildArtifacts@1
  inputs:
    pathtoPublish:(System.DefaultWorkingDirectory)/trivy-report.json'
    artifactName: 'TrivyScanResults'
`

In this example:

1. The pipeline is triggered on changes to the `main` branch.
2 The pipeline runs on an Ubuntu agent with Python 3.x installed.
3.ivy is downloaded, extracted, and added to the PATH.
4. A Trivy scan is on the `my-container-image:latest` Docker image, specifying that the pipeline should fail (with `--exit-code 1`) if any HIGH or CRITICAL are found.
5. The Trivy scan results are published artifacts, here assuming the scan generates a report in JSON format at `trivy-report.json`.

You can modify this example to fit your specific build pipeline structure and requirements.