From 6d80717b35dc3790c1e233975b6bd45fb36270f7 Mon Sep 17 00:00:00 2001 From: Steve Yonkeu Date: Sat, 26 Oct 2024 04:55:40 +0100 Subject: [PATCH] adding links to markdown --- README.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 929c39a..7d8af91 100644 --- a/README.md +++ b/README.md @@ -12,25 +12,25 @@ ## 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. +- **[analytics](analytics)**: Tools and libraries for tracking, analyzing, and visualizing application usage and metrics. +- **[apis](apis)**: Examples for implementing various API types, including REST, GraphQL, gRPC, and more. +- **[caching](caching)**: Different caching mechanisms like Redis, LRU, LFU, and file-based caching to improve application performance. +- **[clean_architecture](clean_architecture)**: Design principles and patterns for structuring Python applications in a maintainable and scalable way. +- **[database](database)**: Implementations for connecting and interacting with multiple database types, including MySQL, PostgreSQL, MongoDB, Redis, and more. +- **[deployments](deployments)**: Deployment scripts and tools for setting up and scaling applications across environments. +- **[docker](docker)**: Docker configurations to containerize services, making deployments easier and more consistent. +- **[emails](emails)**: Code examples for integrating with popular email services like AWS SES, Mailgun, and SendGrid. +- **[env_secrets](env_secrets)**: Techniques for securely managing environment variables and sensitive data in Python applications. +- **[IaC (Infrastructure as Code](IaaC))**: Scripts for automating infrastructure provisioning and management using tools like Terraform and CloudFormation. +- **[logging](logging)**: Logging solutions for tracking application events, errors, and performance metrics. +- **[machine_learning](machine_learning)**: Modules for integrating machine learning models and handling tasks related to data processing and predictions. +- **[monitoring](monitoring)**: Tools for tracking system and application performance, including Prometheus, Grafana, and Datadog integrations. +- **[payments](payments)**: Implementations for handling payments with various providers such as Stripe, PayPal, and local options like M-Pesa. +- **[queueing](queueing)**: Message queue implementations with RabbitMQ, Redis Queue, and Celery for handling background tasks. +- **[scaling](scaling)**: Techniques and tools to scale Python applications effectively, including load balancing and horizontal scaling. +- **[security](security)**: Security features such as encryption, authentication, rate limiting, and best practices for securing APIs. +- **[sms](sms)**: Code for sending SMS using providers like Twilio, Nexmo, AWS SNS, and others. +- **[storage](storage)**: Integrations with cloud storage providers like AWS S3, Google Cloud Storage, and Azure Blob. ## Getting Started