Set up Monitoring Systems for Data Quality Metrics:

[zepor]

            ○ Time: 1 week
    ○ Tools Required: Azure Monitor, Prometheus, Grafana
    ○ Steps:
        1. Set up real-time monitoring using Azure Monitor.
            □ Configure dashboards and alerts for monitoring data flows.
        2. Integrate Prometheus for collecting metrics.
            □ Set up Prometheus for collecting and storing metrics.
        3. Configure Grafana dashboards for real-time insights.
            □ Develop and configure Grafana dashboards for data quality metrics visualization.
        4. Store monitoring credentials in GitHub Secrets.
            □ Secrets Needed: PROMETHEUS_ENDPOINT, GRAFANA_API_KEY
    ○ Documentation:
        § Monitoring setup and configuration guides.
        § Grafana dashboard configurations and sample visualizations.
    ○ Major Milestone: Data quality monitoring systems set up.
    ○ GitHub Issue:

Set up Monitoring Systems for Data Quality Metrics

Description: Implement monitoring systems for tracking data quality. Tasks:

• Set up Azure Monitor and configure real-time monitoring.
• Integrate Prometheus for metrics collection and configure Grafana for real-time insights.
• Store necessary credentials in GitHub Secrets: PROMETHEUS_ENDPOINT, GRAFANA_API_KEY. Milestone: Data quality monitoring systems set up.

[codeautopilot[bot]]

Potential solution

To set up a comprehensive monitoring system for data quality metrics using Azure Monitor, Prometheus, and Grafana, we need to follow a structured approach. This involves setting up real-time monitoring with Azure Monitor, integrating Prometheus for metrics collection, configuring Grafana for visualization, and securely storing credentials in GitHub Secrets. The solution will be implemented through a combination of documentation, scripts, and GitHub Actions workflows.

How to implement

Step 1: Documentation for GitHub Secrets

File: backend-container/docs/github_secrets.md

Create detailed documentation on how to store PROMETHEUS_ENDPOINT and GRAFANA_API_KEY in GitHub Secrets.

## Storing Credentials in GitHub Secrets

### Overview
This guide will walk you through the process of storing sensitive credentials, such as `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY`, in GitHub Secrets. GitHub Secrets allow you to securely store and manage sensitive information needed by your workflows.

### Steps to Store Secrets

1. **Navigate to Your Repository on GitHub:**
   - Open your web browser and go to your GitHub repository.

2. **Access the Settings:**
   - Click on the `Settings` tab located at the top of the repository page.

3. **Go to Secrets:**
   - In the left sidebar, click on `Secrets` under the `Security` section.

4. **Add a New Secret:**
   - Click on the `New repository secret` button.

5. **Enter the Secret Name and Value:**
   - For `PROMETHEUS_ENDPOINT`:
     - In the `Name` field, enter `PROMETHEUS_ENDPOINT`.
     - In the `Value` field, enter the endpoint URL for Prometheus.
   - For `GRAFANA_API_KEY`:
     - In the `Name` field, enter `GRAFANA_API_KEY`.
     - In the `Value` field, enter the API key for Grafana.

6. **Save the Secret:**
   - Click the `Add secret` button to save the secret.

### Example

#### Adding `PROMETHEUS_ENDPOINT`
1. Navigate to your repository on GitHub.
2. Click on `Settings`.
3. In the left sidebar, click on `Secrets`.
4. Click on `New repository secret`.
5. Enter `PROMETHEUS_ENDPOINT` in the `Name` field.
6. Enter the Prometheus endpoint URL in the `Value` field.
7. Click `Add secret`.

#### Adding `GRAFANA_API_KEY`
1. Navigate to your repository on GitHub.
2. Click on `Settings`.
3. In the left sidebar, click on `Secrets`.
4. Click on `New repository secret`.
5. Enter `GRAFANA_API_KEY` in the `Name` field.
6. Enter the Grafana API key in the `Value` field.
7. Click `Add secret`.

### Using Secrets in GitHub Actions

Once the secrets are stored, you can use them in your GitHub Actions workflows. Here is an example of how to reference these secrets in a workflow file:

```yaml
name: Monitoring Setup

on: [push]

jobs:
  setup-monitoring:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
        uses: actions/checkout@v2

      - name: Set up Monitoring Systems
        env:
          PROMETHEUS_ENDPOINT: ${{ secrets.PROMETHEUS_ENDPOINT }}
          GRAFANA_API_KEY: ${{ secrets.GRAFANA_API_KEY }}
        run: |
          # Your setup script here
          echo "Setting up monitoring systems..."

Conclusion

By following these steps, you can securely store and manage your PROMETHEUS_ENDPOINT and GRAFANA_API_KEY in GitHub Secrets, ensuring that sensitive information is protected while being accessible to your workflows.

## Step 2: GitHub Actions Workflow
### File: `backend-container/.github/workflows/monitoring_setup.yml`
Create a GitHub Actions workflow to automate the setup of Azure Monitor, Prometheus, and Grafana, and store credentials in GitHub Secrets.

```yaml
name: Monitoring Setup

on:
  push:
    branches:
      - main
  workflow_dispatch:

jobs:
  setup-monitoring:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout repository
      uses: actions/checkout@v2

    - name: Set up Azure Monitor
      run: |
        curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash
        echo "${{ secrets.AZURE_CREDENTIALS }}" | az login --service-principal --username <client-id> --password <client-secret> --tenant <tenant-id>
        az monitor metrics alert create --name "DataQualityAlert" --resource-group <resource-group> --scopes <resource-id> --condition "avg Percentage CPU > 90" --window-size 5m --evaluation-frequency 1m --action <action-group>

    - name: Set up Prometheus
      run: |
        wget https://github.com/prometheus/prometheus/releases/download/v2.26.0/prometheus-2.26.0.linux-amd64.tar.gz
        tar xvfz prometheus-*.tar.gz
        cd prometheus-*
        ./prometheus --config.file=prometheus.yml &
        echo "::set-output name=prometheus_endpoint::http://localhost:9090"

    - name: Set up Grafana
      run: |
        sudo apt-get install -y adduser libfontconfig1
        wget https://dl.grafana.com/oss/release/grafana_7.5.2_amd64.deb
        sudo dpkg -i grafana_7.5.2_amd64.deb
        sudo systemctl start grafana-server
        sudo systemctl enable grafana-server
        curl -X POST -H "Content-Type: application/json" -d '{"name":"DataQualityDashboard","type":"prometheus","url":"http://localhost:9090","access":"proxy","basicAuth":false}' http://admin:admin@localhost:3000/api/datasources
        GRAFANA_API_KEY=$(curl -X POST -H "Content-Type: application/json" -d '{"name":"api_key","role":"Admin"}' http://admin:admin@localhost:3000/api/auth/keys | jq -r '.key')
        echo "::set-output name=grafana_api_key::$GRAFANA_API_KEY"

    - name: Store credentials in GitHub Secrets
      uses: google/secrets-sync-action@v1
      with:
        secrets: |
          PROMETHEUS_ENDPOINT=${{ steps.setup-monitoring.outputs.prometheus_endpoint }}
          GRAFANA_API_KEY=${{ steps.setup-monitoring.outputs.grafana_api_key }}

Step 3: Monitoring Setup Script

File: backend-container/src/utils/monitoring_setup.py

Implement the setup script for Azure Monitor, Prometheus, and Grafana.

import os
import requests

def setup_azure_monitor():
    print("Setting up Azure Monitor...")

def setup_prometheus():
    print("Setting up Prometheus...")

def setup_grafana():
    grafana_api_key = os.getenv('GRAFANA_API_KEY')
    prometheus_endpoint = os.getenv('PROMETHEUS_ENDPOINT')

    if not grafana_api_key or not prometheus_endpoint:
        raise ValueError("GRAFANA_API_KEY and PROMETHEUS_ENDPOINT must be set in environment variables")

    headers = {
        'Authorization': f'Bearer {grafana_api_key}',
        'Content-Type': 'application/json'
    }

    data_source_payload = {
        "name": "Prometheus",
        "type": "prometheus",
        "url": prometheus_endpoint,
        "access": "proxy",
        "basicAuth": False
    }

    response = requests.post('http://localhost:3000/api/datasources', json=data_source_payload, headers=headers)
    if response.status_code == 200:
        print("Grafana data source created successfully")
    else:
        print(f"Failed to create Grafana data source: {response.content}")

    print("Setting up Grafana dashboards...")

def main():
    setup_azure_monitor()
    setup_prometheus()
    setup_grafana()

if __name__ == "__main__":
    main()

Step 4: Script for Storing GitHub Secrets

File: backend-container/src/utils/github_secrets.py

Implement the script to store PROMETHEUS_ENDPOINT and GRAFANA_API_KEY in GitHub Secrets.

import base64
import json
import requests
from cryptography.hazmat.primitives.asymmetric import rsa
from cryptography.hazmat.primitives import serialization, hashes
from cryptography.hazmat.primitives.asymmetric import padding

GITHUB_REPO = "your-username/your-repo"
GITHUB_TOKEN = "your-github-token"
PROMETHEUS_ENDPOINT = "your-prometheus-endpoint"
GRAFANA_API_KEY = "your-grafana-api-key"

def get_public_key():
    url = f"https://api.github.com/repos/{GITHUB_REPO}/actions/secrets/public-key"
    headers = {
        "Authorization": f"token {GITHUB_TOKEN}",
        "Accept": "application/vnd.github.v3+json"
    }
    response = requests.get(url, headers=headers)
    response.raise_for_status()
    return response.json()

def encrypt_secret(public_key, secret_value):
    public_key_bytes = base64.b64decode(public_key["key"])
    public_key = serialization.load_der_public_key(public_key_bytes)
    encrypted = public_key.encrypt(
        secret_value.encode(),
        padding.OAEP(
            mgf=padding.MGF1(algorithm=hashes.SHA256()),
            algorithm=hashes.SHA256(),
            label=None
        )
    )
    return base64.b64encode(encrypted).decode()

def store_secret(secret_name, encrypted_value, key_id):
    url = f"https://api.github.com/repos/{GITHUB_REPO}/actions/secrets/{secret_name}"
    headers = {
        "Authorization": f"token {GITHUB_TOKEN}",
        "Accept": "application/vnd.github.v3+json"
    }
    data = {
        "encrypted_value": encrypted_value,
        "key_id": key_id
    }
    response = requests.put(url, headers=headers, data=json.dumps(data))
    response.raise_for_status()

def main():
    public_key = get_public_key()
    key_id = public_key["key_id"]

    encrypted_prometheus_endpoint = encrypt_secret(public_key, PROMETHEUS_ENDPOINT)
    encrypted_grafana_api_key = encrypt_secret(public_key, GRAFANA_API_KEY)

    store_secret("PROMETHEUS_ENDPOINT", encrypted_prometheus_endpoint, key_id)
    store_secret("GRAFANA_API_KEY", encrypted_grafana_api_key, key_id)

if __name__ == "__main__":
    main()

Step 5: Monitoring Setup Documentation

File: backend-container/docs/monitoring_setup.md

Create comprehensive documentation for setting up and configuring Azure Monitor, Prometheus, and Grafana.

# Monitoring Setup and Configuration Guide

## Introduction
This document provides a step-by-step guide to set up and configure Azure Monitor, Prometheus, and Grafana for monitoring data quality metrics.

## Prerequisites
- Azure account with necessary permissions
- Prometheus installed and running
- Grafana installed and running
- GitHub repository with access to GitHub Secrets

## Setting Up Azure Monitor

### Create an Azure Monitor Resource
1. Navigate to the Azure portal.
2. Create a new Azure Monitor resource.
3. Configure the resource to monitor your data flows.

### Configure Dashboards and Alerts
1. Go to the Azure Monitor resource.
2. Set up dashboards to visualize data flows.
3. Configure alerts to notify you of any anomalies or issues.

## Integrating Prometheus

### Install Prometheus
Follow the [official Prometheus installation guide](https://prometheus.io/docs/prometheus/latest/installation/) for your operating system.

### Configure Prometheus
Edit the `prometheus.yml` configuration file to scrape metrics from your data sources.

Example configuration:
```yaml
scrape_configs:
  - job_name: 'data_quality_metrics'
    static_configs:
      - targets: ['<your_data_source_endpoint>']

Start Prometheus

Run Prometheus using the configured prometheus.yml file.

Setting Up Grafana

Install Grafana

Follow the official Grafana installation guide for your operating system.

Configure Grafana Data Source

1. Log in to Grafana.
2. Add Prometheus as a data source:
   • Navigate to Configuration > Data Sources.
   • Add a new data source and select Prometheus.
   • Enter the Prometheus endpoint URL.

Create Grafana Dashboards

1. Create new dashboards to visualize data quality metrics.
2. Example panels to include:
   • Data flow rates
   • Error rates
   • Data completeness

Sample Visualizations

Provide screenshots or JSON configurations of sample dashboards.

Storing Monitoring Credentials in GitHub Secrets

Add Secrets to GitHub

1. Navigate to your GitHub repository.
2. Go to Settings > Secrets and variables > Actions.
3. Add the following secrets:
   • PROMETHEUS_ENDPOINT
   • GRAFANA_API_KEY

Conclusion

By following this guide, you will have a robust monitoring system in place to track data quality metrics using Azure Monitor, Prometheus, and Grafana.

Additional Resources

• Azure Monitor Documentation
• Prometheus Documentation
• Grafana Documentation

By following these steps, you will have a comprehensive setup for monitoring data quality metrics using Azure Monitor, Prometheus, and Grafana, with secure storage of credentials in GitHub Secrets.

Click here to create a Pull Request with the proposed solution

Files used for this task:

Changes on backend-container/docs/github_secrets.md

To address the task for the file `backend-container/docs/github_secrets.md`, you need to write detailed documentation on how to store `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY` in GitHub Secrets. Here is a step-by-step guide that you can include in the documentation: --- ## Storing Credentials in GitHub Secrets ### Overview This guide will walk you through the process of storing sensitive credentials, such as `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY`, in GitHub Secrets. GitHub Secrets allow you to securely store and manage sensitive information needed by your workflows. ### Steps to Store Secrets 1. **Navigate to Your Repository on GitHub:** - Open your web browser and go to your GitHub repository. 2. **Access the Settings:** - Click on the `Settings` tab located at the top of the repository page. 3. **Go to Secrets:** - In the left sidebar, click on `Secrets` under the `Security` section. 4. **Add a New Secret:** - Click on the `New repository secret` button. 5. **Enter the Secret Name and Value:** - For `PROMETHEUS_ENDPOINT`: - In the `Name` field, enter `PROMETHEUS_ENDPOINT`. - In the `Value` field, enter the endpoint URL for Prometheus. - For `GRAFANA_API_KEY`: - In the `Name` field, enter `GRAFANA_API_KEY`. - In the `Value` field, enter the API key for Grafana. 6. **Save the Secret:** - Click the `Add secret` button to save the secret. ### Example #### Adding `PROMETHEUS_ENDPOINT` 1. Navigate to your repository on GitHub. 2. Click on `Settings`. 3. In the left sidebar, click on `Secrets`. 4. Click on `New repository secret`. 5. Enter `PROMETHEUS_ENDPOINT` in the `Name` field. 6. Enter the Prometheus endpoint URL in the `Value` field. 7. Click `Add secret`. #### Adding `GRAFANA_API_KEY` 1. Navigate to your repository on GitHub. 2. Click on `Settings`. 3. In the left sidebar, click on `Secrets`. 4. Click on `New repository secret`. 5. Enter `GRAFANA_API_KEY` in the `Name` field. 6. Enter the Grafana API key in the `Value` field. 7. Click `Add secret`. ### Using Secrets in GitHub Actions Once the secrets are stored, you can use them in your GitHub Actions workflows. Here is an example of how to reference these secrets in a workflow file: ```yaml name: Monitoring Setup on: [push] jobs: setup-monitoring: runs-on: ubuntu-latest steps: - name: Checkout code uses: actions/checkout@v2 - name: Set up Monitoring Systems env: PROMETHEUS_ENDPOINT: ${{ secrets.PROMETHEUS_ENDPOINT }} GRAFANA_API_KEY: ${{ secrets.GRAFANA_API_KEY }} run: | # Your setup script here echo "Setting up monitoring systems..." ``` ### Conclusion By following these steps, you can securely store and manage your `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY` in GitHub Secrets, ensuring that sensitive information is protected while being accessible to your workflows. --- This documentation provides a clear and detailed guide for users to store their credentials in GitHub Secrets, ensuring they can securely manage sensitive information required for their monitoring setup.

Changes on backend-container/.github/workflows/monitoring_setup.yml

To create a GitHub Actions workflow that automates the setup of Azure Monitor, Prometheus, and Grafana, and stores credentials in GitHub Secrets, follow these steps: 1. **Create the GitHub Actions Workflow File**: - The file should be named `monitoring_setup.yml` and placed in the `.github/workflows/` directory. - This workflow will include steps to set up Azure Monitor, Prometheus, and Grafana, and store the necessary credentials in GitHub Secrets. 2. **Define the Workflow Structure**: - The workflow should be triggered manually or on a specific event (e.g., push to a specific branch). - It should include jobs for setting up Azure Monitor, Prometheus, and Grafana, and storing credentials. 3. **Implement the Workflow Steps**: - Each job should include steps to install necessary tools, configure the monitoring systems, and store credentials. Here is a concrete example of what the `monitoring_setup.yml` file might look like: ```yaml name: Monitoring Setup on: push: branches: - main workflow_dispatch: jobs: setup-monitoring: runs-on: ubuntu-latest steps: - name: Checkout repository uses: actions/checkout@v2 - name: Set up Azure Monitor run: | # Install Azure CLI curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash # Login to Azure (assumes AZURE_CREDENTIALS secret is set) echo "${{ secrets.AZURE_CREDENTIALS }}" | az login --service-principal --username --password --tenant # Set up Azure Monitor (customize as needed) az monitor metrics alert create --name "DataQualityAlert" --resource-group --scopes --condition "avg Percentage CPU > 90" --window-size 5m --evaluation-frequency 1m --action - name: Set up Prometheus run: | # Install Prometheus wget https://github.com/prometheus/prometheus/releases/download/v2.26.0/prometheus-2.26.0.linux-amd64.tar.gz tar xvfz prometheus-*.tar.gz cd prometheus-* ./prometheus --config.file=prometheus.yml & # Store Prometheus endpoint in GitHub Secrets echo "::set-output name=prometheus_endpoint::http://localhost:9090" - name: Set up Grafana run: | # Install Grafana sudo apt-get install -y adduser libfontconfig1 wget https://dl.grafana.com/oss/release/grafana_7.5.2_amd64.deb sudo dpkg -i grafana_7.5.2_amd64.deb sudo systemctl start grafana-server sudo systemctl enable grafana-server # Configure Grafana (customize as needed) curl -X POST -H "Content-Type: application/json" -d '{"name":"DataQualityDashboard","type":"prometheus","url":"http://localhost:9090","access":"proxy","basicAuth":false}' http://admin:admin@localhost:3000/api/datasources # Store Grafana API key in GitHub Secrets GRAFANA_API_KEY=$(curl -X POST -H "Content-Type: application/json" -d '{"name":"api_key","role":"Admin"}' http://admin:admin@localhost:3000/api/auth/keys | jq -r '.key') echo "::set-output name=grafana_api_key::$GRAFANA_API_KEY" - name: Store credentials in GitHub Secrets uses: google/secrets-sync-action@v1 with: secrets: | PROMETHEUS_ENDPOINT=${{ steps.setup-monitoring.outputs.prometheus_endpoint }} GRAFANA_API_KEY=${{ steps.setup-monitoring.outputs.grafana_api_key }} ``` ### Explanation: - **Trigger**: The workflow is triggered on a push to the `main` branch or manually via the `workflow_dispatch` event. - **Jobs**: - **Checkout repository**: Uses the `actions/checkout@v2` action to check out the repository. - **Set up Azure Monitor**: Installs Azure CLI, logs in using service principal credentials, and sets up Azure Monitor alerts. - **Set up Prometheus**: Downloads and runs Prometheus, and stores the Prometheus endpoint in GitHub Secrets. - **Set up Grafana**: Installs and configures Grafana, creates a data source, and stores the Grafana API key in GitHub Secrets. - **Store credentials in GitHub Secrets**: Uses the `google/secrets-sync-action@v1` action to store the Prometheus endpoint and Grafana API key in GitHub Secrets. This workflow provides a comprehensive setup for monitoring systems and ensures that necessary credentials are securely stored in GitHub Secrets. Adjust the commands and configurations as needed to fit your specific environment and requirements.

Changes on backend-container/src/utils/monitoring_setup.py

To implement the setup script for Azure Monitor, Prometheus, and Grafana in the `backend-container/src/utils/monitoring_setup.py` file, follow these steps: 1. **Set Up Azure Monitor**: - Configure dashboards and alerts for monitoring data flows. 2. **Integrate Prometheus**: - Set up Prometheus for collecting and storing metrics. 3. **Configure Grafana Dashboards**: - Develop and configure Grafana dashboards for data quality metrics visualization. Here is a concrete implementation plan for the `monitoring_setup.py` file: ### Step-by-Step Implementation 1. **Import Necessary Libraries**: - Import libraries for interacting with Azure Monitor, Prometheus, and Grafana APIs. 2. **Define Functions for Each Setup Task**: - Create functions to set up Azure Monitor, Prometheus, and Grafana. 3. **Main Function to Execute the Setup**: - Create a main function that calls the setup functions in sequence. ### Example Code ```python import os import requests # Azure Monitor setup def setup_azure_monitor(): # Placeholder for Azure Monitor setup code # This might include creating dashboards, setting up alerts, etc. print("Setting up Azure Monitor...") # Prometheus setup def setup_prometheus(): # Placeholder for Prometheus setup code # This might include configuring Prometheus to scrape metrics from your services print("Setting up Prometheus...") # Grafana setup def setup_grafana(): grafana_api_key = os.getenv('GRAFANA_API_KEY') prometheus_endpoint = os.getenv('PROMETHEUS_ENDPOINT') if not grafana_api_key or not prometheus_endpoint: raise ValueError("GRAFANA_API_KEY and PROMETHEUS_ENDPOINT must be set in environment variables") headers = { 'Authorization': f'Bearer {grafana_api_key}', 'Content-Type': 'application/json' } # Example of creating a Grafana data source for Prometheus data_source_payload = { "name": "Prometheus", "type": "prometheus", "url": prometheus_endpoint, "access": "proxy", "basicAuth": False } response = requests.post('http://localhost:3000/api/datasources', json=data_source_payload, headers=headers) if response.status_code == 200: print("Grafana data source created successfully") else: print(f"Failed to create Grafana data source: {response.content}") # Placeholder for creating Grafana dashboards print("Setting up Grafana dashboards...") def main(): setup_azure_monitor() setup_prometheus() setup_grafana() if __name__ == "__main__": main() ``` ### Explanation 1. **Importing Libraries**: - `os` for accessing environment variables. - `requests` for making HTTP requests to Grafana API. 2. **Azure Monitor Setup**: - Placeholder function `setup_azure_monitor` for configuring Azure Monitor. 3. **Prometheus Setup**: - Placeholder function `setup_prometheus` for configuring Prometheus. 4. **Grafana Setup**: - `setup_grafana` function to configure Grafana, including creating a data source for Prometheus and setting up dashboards. - Uses environment variables `GRAFANA_API_KEY` and `PROMETHEUS_ENDPOINT` for authentication and configuration. 5. **Main Function**: - Calls the setup functions in sequence to perform the complete setup. ### Next Steps 1. **Implement Detailed Azure Monitor and Prometheus Setup**: - Replace placeholders with actual code for setting up Azure Monitor and Prometheus. 2. **Enhance Grafana Setup**: - Add code to create and configure Grafana dashboards based on your specific requirements. 3. **Testing**: - Test the script to ensure it correctly sets up the monitoring systems. By following this plan, you will have a comprehensive setup script for Azure Monitor, Prometheus, and Grafana, including the configuration of dashboards and alerts.

Changes on backend-container/src/utils/github_secrets.py

To implement the script for storing `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY` in GitHub Secrets, you can use the GitHub API. Below is a step-by-step guide and the corresponding Python code to achieve this. ### Steps to Implement the Script 1. **Install Required Libraries**: - Ensure you have the `requests` library installed to make HTTP requests to the GitHub API. - You can install it using `pip install requests`. 2. **Create a GitHub Personal Access Token**: - You need a GitHub Personal Access Token with the `repo` and `admin:repo_hook` permissions to access and modify secrets. 3. **Encrypt the Secrets**: - GitHub requires secrets to be encrypted before storing them. You can use the `cryptography` library to handle encryption. 4. **Store the Secrets**: - Use the GitHub API to store the encrypted secrets in the repository. ### Python Code for `github_secrets.py` ```python import base64 import json import requests from cryptography.hazmat.primitives.asymmetric import rsa from cryptography.hazmat.primitives import serialization, hashes from cryptography.hazmat.primitives.asymmetric import padding # Replace these with your actual values GITHUB_REPO = "your-username/your-repo" GITHUB_TOKEN = "your-github-token" PROMETHEUS_ENDPOINT = "your-prometheus-endpoint" GRAFANA_API_KEY = "your-grafana-api-key" def get_public_key(): url = f"https://api.github.com/repos/{GITHUB_REPO}/actions/secrets/public-key" headers = { "Authorization": f"token {GITHUB_TOKEN}", "Accept": "application/vnd.github.v3+json" } response = requests.get(url, headers=headers) response.raise_for_status() return response.json() def encrypt_secret(public_key, secret_value): public_key_bytes = base64.b64decode(public_key["key"]) public_key = serialization.load_der_public_key(public_key_bytes) encrypted = public_key.encrypt( secret_value.encode(), padding.OAEP( mgf=padding.MGF1(algorithm=hashes.SHA256()), algorithm=hashes.SHA256(), label=None ) ) return base64.b64encode(encrypted).decode() def store_secret(secret_name, encrypted_value, key_id): url = f"https://api.github.com/repos/{GITHUB_REPO}/actions/secrets/{secret_name}" headers = { "Authorization": f"token {GITHUB_TOKEN}", "Accept": "application/vnd.github.v3+json" } data = { "encrypted_value": encrypted_value, "key_id": key_id } response = requests.put(url, headers=headers, data=json.dumps(data)) response.raise_for_status() def main(): public_key = get_public_key() key_id = public_key["key_id"] encrypted_prometheus_endpoint = encrypt_secret(public_key, PROMETHEUS_ENDPOINT) encrypted_grafana_api_key = encrypt_secret(public_key, GRAFANA_API_KEY) store_secret("PROMETHEUS_ENDPOINT", encrypted_prometheus_endpoint, key_id) store_secret("GRAFANA_API_KEY", encrypted_grafana_api_key, key_id) if __name__ == "__main__": main() ``` ### Explanation 1. **Get Public Key**: - The `get_public_key` function fetches the public key from GitHub, which is used to encrypt the secrets. 2. **Encrypt Secret**: - The `encrypt_secret` function encrypts the secret value using the public key. 3. **Store Secret**: - The `store_secret` function stores the encrypted secret in the GitHub repository using the GitHub API. 4. **Main Function**: - The `main` function orchestrates the process by fetching the public key, encrypting the secrets, and storing them in the repository. ### Next Steps - **Testing**: Test the script in a safe environment to ensure it works as expected. - **Documentation**: Document the usage of this script in the `backend-container/docs/github_secrets.md` file. - **Automation**: Integrate this script into the GitHub Actions workflow defined in `backend-container/.github/workflows/monitoring_setup.yml`. This script will securely store the `PROMETHEUS_ENDPOINT` and `GRAFANA_API_KEY` in GitHub Secrets, ensuring they are available for use in your CI/CD pipelines.

Changes on backend-container/docs/monitoring_setup.md

To address the task for the file `backend-container/docs/monitoring_setup.md`, you need to create comprehensive documentation that guides users through the setup and configuration of Azure Monitor, Prometheus, and Grafana. This documentation should include detailed steps, configurations, and sample visualizations to ensure users can effectively monitor data quality metrics. Here's a structured approach to writing this documentation: ### Monitoring Setup Documentation #### 1. Introduction Provide an overview of the purpose of the document and what users will achieve by following it. #### 2. Prerequisites List the tools and accounts required: - Azure account with necessary permissions - Prometheus installed and running - Grafana installed and running - GitHub repository with access to GitHub Secrets #### 3. Setting Up Azure Monitor ##### 3.1. Create an Azure Monitor Resource - Navigate to the Azure portal. - Create a new Azure Monitor resource. - Configure the resource to monitor your data flows. ##### 3.2. Configure Dashboards and Alerts - Go to the Azure Monitor resource. - Set up dashboards to visualize data flows. - Configure alerts to notify you of any anomalies or issues. #### 4. Integrating Prometheus ##### 4.1. Install Prometheus - Follow the official Prometheus installation guide for your operating system. ##### 4.2. Configure Prometheus - Edit the `prometheus.yml` configuration file to scrape metrics from your data sources. - Example configuration: ```yaml scrape_configs: - job_name: 'data_quality_metrics' static_configs: - targets: [''] ``` ##### 4.3. Start Prometheus - Run Prometheus using the configured `prometheus.yml` file. #### 5. Setting Up Grafana ##### 5.1. Install Grafana - Follow the official Grafana installation guide for your operating system. ##### 5.2. Configure Grafana Data Source - Log in to Grafana. - Add Prometheus as a data source: - Navigate to Configuration > Data Sources. - Add a new data source and select Prometheus. - Enter the Prometheus endpoint URL. ##### 5.3. Create Grafana Dashboards - Create new dashboards to visualize data quality metrics. - Example panels to include: - Data flow rates - Error rates - Data completeness ##### 5.4. Sample Visualizations - Provide screenshots or JSON configurations of sample dashboards. #### 6. Storing Monitoring Credentials in GitHub Secrets ##### 6.1. Add Secrets to GitHub - Navigate to your GitHub repository. - Go to Settings > Secrets and variables > Actions. - Add the following secrets: - `PROMETHEUS_ENDPOINT` - `GRAFANA_API_KEY` #### 7. Conclusion Summarize the steps and emphasize the importance of monitoring data quality. #### 8. Additional Resources Provide links to official documentation for Azure Monitor, Prometheus, and Grafana for further reading. ### Example Content for `monitoring_setup.md` ```markdown # Monitoring Setup and Configuration Guide ## Introduction This document provides a step-by-step guide to set up and configure Azure Monitor, Prometheus, and Grafana for monitoring data quality metrics. ## Prerequisites - Azure account with necessary permissions - Prometheus installed and running - Grafana installed and running - GitHub repository with access to GitHub Secrets ## Setting Up Azure Monitor ### Create an Azure Monitor Resource 1. Navigate to the Azure portal. 2. Create a new Azure Monitor resource. 3. Configure the resource to monitor your data flows. ### Configure Dashboards and Alerts 1. Go to the Azure Monitor resource. 2. Set up dashboards to visualize data flows. 3. Configure alerts to notify you of any anomalies or issues. ## Integrating Prometheus ### Install Prometheus Follow the [official Prometheus installation guide](https://prometheus.io/docs/prometheus/latest/installation/) for your operating system. ### Configure Prometheus Edit the `prometheus.yml` configuration file to scrape metrics from your data sources. Example configuration: ```yaml scrape_configs: - job_name: 'data_quality_metrics' static_configs: - targets: [''] ``` ### Start Prometheus Run Prometheus using the configured `prometheus.yml` file. ## Setting Up Grafana ### Install Grafana Follow the [official Grafana installation guide](https://grafana.com/docs/grafana/latest/installation/) for your operating system. ### Configure Grafana Data Source 1. Log in to Grafana. 2. Add Prometheus as a data source: - Navigate to Configuration > Data Sources. - Add a new data source and select Prometheus. - Enter the Prometheus endpoint URL. ### Create Grafana Dashboards 1. Create new dashboards to visualize data quality metrics. 2. Example panels to include: - Data flow rates - Error rates - Data completeness ### Sample Visualizations Provide screenshots or JSON configurations of sample dashboards. ## Storing Monitoring Credentials in GitHub Secrets ### Add Secrets to GitHub 1. Navigate to your GitHub repository. 2. Go to Settings > Secrets and variables > Actions. 3. Add the following secrets: - `PROMETHEUS_ENDPOINT` - `GRAFANA_API_KEY` ## Conclusion By following this guide, you will have a robust monitoring system in place to track data quality metrics using Azure Monitor, Prometheus, and Grafana. ## Additional Resources - [Azure Monitor Documentation](https://docs.microsoft.com/en-us/azure/azure-monitor/) - [Prometheus Documentation](https://prometheus.io/docs/introduction/overview/) - [Grafana Documentation](https://grafana.com/docs/grafana/latest/) ``` This documentation will help users set up and configure the necessary monitoring systems effectively.

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