initial setup and structure

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/
+.history/

CODE_OF_CONDUCT.md

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+What can we say?
+
+JUST BE NICE TO EACH OTHER!

IaaC/README.md

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+Coming Soon...

LICENSE

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+The MIT License (MIT)
+
+Copyright (c) 2024 Steve Yonkeu (@yokwejuste)
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

README.md

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+<!-- markdownlint-disable MD033 MD041 -->
+<p align="center">
+  <h2 align="center">🚀BACKEND SECRETS </h2>
+</p>
+
+<p align="center">
+    <img src="https://readme-typing-svg.demolab.com?font=Fira+Code&pause=1000&color=2FF700&random=true&width=435&lines=Analytics%2C+API+Setup;Caching%2C+Database+Configuration;Backend+Developments+and+Docker;Email+Setup%2C+Secret+Management;Infrastructure+As+A+Code%2C+Machine+Learning;Logging%2C+Monitoring%2C+Analytics;RESTFul+APIs%2C+GraphQL%2C+SOAP%2C+gRPC;Unit+Testing+and+CI+CD;Webhooks+and+Web+Sockets+working+with+Database;Web+Scraping%2C+Third+party+Payment;Third+party+Storages+and+Security" alt="Typing SVG" />
+</p>
+<!-- markdownlint-enable MD033 MD041 -->
+
+**Backend Secrets** is a comprehensive collection of Python implementations covering various backend functionalities, organized into distinct modules. This repository is designed to serve as a one-stop resource for backend developers looking to streamline common tasks, enhance security, and optimize performance in their applications. Each folder addresses a specific area of backend development, from caching and authentication to machine learning and server scaling.
+
+## Directory Structure
+
+- **analytics**: Tools and libraries for tracking, analyzing, and visualizing application usage and metrics.
+- **apis**: Examples for implementing various API types, including REST, GraphQL, gRPC, and more.
+- **caching**: Different caching mechanisms like Redis, LRU, LFU, and file-based caching to improve application performance.
+- **clean_architecture**: Design principles and patterns for structuring Python applications in a maintainable and scalable way.
+- **database**: Implementations for connecting and interacting with multiple database types, including MySQL, PostgreSQL, MongoDB, Redis, and more.
+- **deployments**: Deployment scripts and tools for setting up and scaling applications across environments.
+- **docker**: Docker configurations to containerize services, making deployments easier and more consistent.
+- **emails**: Code examples for integrating with popular email services like AWS SES, Mailgun, and SendGrid.
+- **env_secrets**: Techniques for securely managing environment variables and sensitive data in Python applications.
+- **IaC (Infrastructure as Code)**: Scripts for automating infrastructure provisioning and management using tools like Terraform and CloudFormation.
+- **logging**: Logging solutions for tracking application events, errors, and performance metrics.
+- **machine_learning**: Modules for integrating machine learning models and handling tasks related to data processing and predictions.
+- **monitoring**: Tools for tracking system and application performance, including Prometheus, Grafana, and Datadog integrations.
+- **payments**: Implementations for handling payments with various providers such as Stripe, PayPal, and local options like M-Pesa.
+- **queueing**: Message queue implementations with RabbitMQ, Redis Queue, and Celery for handling background tasks.
+- **scaling**: Techniques and tools to scale Python applications effectively, including load balancing and horizontal scaling.
+- **security**: Security features such as encryption, authentication, rate limiting, and best practices for securing APIs.
+- **sms**: Code for sending SMS using providers like Twilio, Nexmo, AWS SNS, and others.
+- **storage**: Integrations with cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob.
+
+## Getting Started
+
+1. **Clone the repository**:
+   ```bash
+   git clone https://github.com/<your-username>/backend-secrets.git
+   cd backend-secrets
+   ```
+
+2. **Install Dependencies**: Each folder contains instructions for installing necessary libraries. You can also create a virtual environment and install dependencies separately for each module.
+
+3. **Run Examples**: Navigate to the desired module and follow the instructions in its README to run specific examples.
+
+## Contributing
+
+Contributions are welcome! If you have a feature to add, a bug to fix, or an improvement suggestion, please submit a pull request with a description of the changes.
+
+## License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

SECURITY.md

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+# Security
+
+As for the security, I personally think we will opt into the best practices for securing our backend services. Please if anyone has any suggestions kindly raise an issue or email me at `[email protected]`.

analytics/README.md

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+# Analytics
+
+This folder contains Python implementations for integrating various analytics tools into backend applications. Each script provides functions to send events, track metrics, and monitor application usage using different analytics platforms.
+
+## AWS CloudWatch
+
+AWS CloudWatch is a monitoring and observability service from AWS that allows you to collect and track metrics, set alarms, and automatically respond to changes in AWS resources.
+
+- **File**: `aws_cloudwatch_analytics.py`
+- **Setup**:
+  1. Install the AWS SDK: `pip install boto3`.
+  2. Configure AWS credentials with access to CloudWatch.
+
+- **Example Usage**:
+  ```python
+  from aws_cloudwatch_analytics import put_custom_metric
+  put_custom_metric("MyAppNamespace", "UserLogins", 1)
+  ```
+
+## Azure Monitor
+
+Azure Monitor provides full-stack monitoring for applications, infrastructure, and network. It includes features like metrics tracking, alerting, and visualization.
+
+- **File**: `azure_monitor_analytics.py`
+- **Setup**:
+  1. Install Azure SDK: `pip install azure-monitor-query azure-identity`.
+  2. Configure Azure credentials (client ID, tenant ID, and client secret) for authentication.
+
+- **Example Usage**:
+  ```python
+  from azure_monitor_analytics import query_metrics
+  query_metrics("<resource_id>")
+  ```
+
+## Google Analytics
+
+Google Analytics allows you to track website and application usage using events, page views, and other metrics through the Measurement Protocol.
+
+- **File**: `google_analytics.py`
+- **Setup**:
+  1. No additional installation required, but you’ll need a Google Analytics tracking ID.
+
+- **Example Usage**:
+  ```python
+  from google_analytics import send_event
+  send_event("category", "action", "label", 10)
+  ```
+
+## Mixpanel
+
+Mixpanel is an advanced analytics tool focused on tracking user interactions and product usage to provide insights into how users engage with your application.
+
+- **File**: `mixpanel_analytics.py`
+- **Setup**:
+  1. Install the Mixpanel client: `pip install mixpanel`.
+
+- **Example Usage**:
+  ```python
+  from mixpanel_analytics import track_event
+  track_event("UserSignUp", "user_123", {"plan": "premium"})
+  ```
+
+## Sentry
+
+Sentry is primarily an error tracking tool, but it also provides application performance monitoring. It’s ideal for capturing and reporting exceptions in real-time.
+
+- **File**: `sentry_analytics.py`
+- **Setup**:
+  1. Install the Sentry SDK: `pip install sentry-sdk`.
+  2. Initialize Sentry with your project’s DSN.
+
+- **Example Usage**:
+  ```python
+  from sentry_analytics import log_error
+  log_error()
+  ```
+
+## How to Use
+
+1. Clone the repository and navigate to the `analytics` folder.
+2. Install any required dependencies as mentioned in each section.
+3. Configure your credentials for each service (e.g., AWS access keys, Google Analytics tracking ID).
+4. Run the scripts to integrate analytics tracking into your application.
+
+## Contributing
+
+Feel free to contribute by adding more analytics tools or improving existing ones. Please submit a pull request with a detailed description of your changes.

analytics/aws_cloudwatch_analytics.py

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+import os
+
+import boto3
+from dotenv import load_dotenv
+
+load_dotenv()
+
+AWS_REGION = os.getenv('AWS_REGION')
+
+# Initialize CloudWatch client
+cloudwatch = boto3.client('cloudwatch', region_name=AWS_REGION)
+
+
+def put_custom_metric(namespace, metric_name, value):
+    response = cloudwatch.put_metric_data(
+        Namespace=namespace,
+        MetricData=[
+            {
+                'MetricName': metric_name,
+                'Value': value,
+                'Unit': 'Count'
+            },
+        ]
+    )
+    print(f"Metric sent: {metric_name} - Value: {value}")
+
+
+# Example usage
+put_custom_metric("MyAppNamespace", "UserLogins", 1)