abhi-io

## DevOps Exercise TF
# DevOps Exercise: Secure Serverless Data Ingestion Pipeline

Objective

Provision a secure, event-driven data pipeline entirely using Terraform. The pipeline will use an AWS Lambda function, triggered by an S3 event, to ingest data into a relational database (RDS). Critically, all database credentials must be managed securely through AWS Secrets Manager, and the Lambda must operate within a VPC to connect to the private RDS instance.

This exercise validates your ability to provision a complex, secure, and networked infrastructure stack using IaC.

Scenario

## old.kseb.in_Archive.json
{
  "log": {
    "version": "1.2",
    "creator": {
      "name": "Firefox",
      "version": "139.0.4"
    },
    "browser": {
      "name": "Firefox",
      "version": "139.0.4"

## del_test2_docker-compose.txt
DevOps Exercise: Scalable Data Pipeline with AWS, Prometheus, Grafana, and SFTP


Objective
Deploy a data ingestion and processing pipeline using Docker Compose, AWS services, and monitoring tools. The pipeline should securely transfer data from an SFTP server, process it, store it in an AWS S3 bucket, and provide real-time monitoring of performance and health. This exercise tests your understanding of containerization, cloud services, monitoring, and secure data handling.


Scenario
You are tasked with building a data pipeline that ingests log files from an external SFTP server, processes them using a custom Python script, and stores the processed data in an AWS S3 bucket. The pipeline needs to be scalable, monitored, and secure.

## get_cf_turnstile_data.py
import requests
import json

account_id = "1ed--61e"
url = f"https://api.cloudflare.com/client/v4/accounts/{account_id}/challenges/widgets"
headers = {
    "X-Auth-Email": "a@gmail.com",
    "X-Auth-Key": "504--1e6"  #not API token
}

## del_test1_docker-compose

DevOps Exercise: Multi-Service Deployment with Docker Compose
Objective

Deploy a scalable, multi-service e-commerce setup using Docker Compose that includes a reverse proxy, product management service, caching, and a database. Your setup should be secure, resilient, and able to handle simulated load. This exercise tests container orchestration, secure configuration management, and performance monitoring.
Scenario

You're setting up the backend infrastructure for a product catalog in an e-commerce platform. The platform includes:

    1.API Gateway (Nginx): A reverse proxy that routes requests to the Product Service and caches frequently accessed data.

## paramu html

<!DOCTYPE html>
<html>
<head>
    <title>Payslip - Consolidated</title>
    <style>
        table {
            width: 100%;
            border-collapse: collapse;
        }

## nginx conf - ip server
# Define an upstream block for backend services
upstream api_backend {
    server localhost:6565;
}

# HTTP server block to handle requests
server {
    listen 80;
    server_name ;

## WordPress-Dokerfile
To set up multiple WordPress instances with separate databases using Docker, you'll need to modify the Docker commands to create additional containers for WordPress and MariaDB. Here's how you can set it up for three websites:

1. Create Docker networks:
```bash
docker network create wordpress-network1
docker network create wordpress-network2
docker network create wordpress-network3
```

2. Create volumes for each WordPress instance and database:

## gist:56f084d11201469a6973b0205b62cc0f
import os
from openai import OpenAI

client = OpenAI(
    api_key = os.getenv("OPENAI_API_KEY"),
)

completion = client.chat.completions.create( # Change the method
  model = "gpt-3.5-turbo",
  messages = [ # Change the prompt parameter to messages parameter

## threeJs - Basic3d
import React, { useEffect } from 'react';
import * as THREE from 'three';
import { GLTFLoader } from 'three/examples/jsm/loaders/GLTFLoader';
import { OrbitControls } from 'three/examples/jsm/controls/OrbitControls';

function Basic3d() {
  useEffect(() => {
    // Scene
    const scene = new THREE.Scene();
    scene.background = new THREE.Color(0xaaaaaa); // Optional: Change scene background
	# DevOps Exercise: Secure Serverless Data Ingestion Pipeline

	Objective

	Provision a secure, event-driven data pipeline entirely using Terraform. The pipeline will use an AWS Lambda function, triggered by an S3 event, to ingest data into a relational database (RDS). Critically, all database credentials must be managed securely through AWS Secrets Manager, and the Lambda must operate within a VPC to connect to the private RDS instance.

	This exercise validates your ability to provision a complex, secure, and networked infrastructure stack using IaC.

	Scenario
	{
	"log": {
	"version": "1.2",
	"creator": {
	"name": "Firefox",
	"version": "139.0.4"
	},
	"browser": {
	"name": "Firefox",
	"version": "139.0.4"
	DevOps Exercise: Scalable Data Pipeline with AWS, Prometheus, Grafana, and SFTP


	Objective
	Deploy a data ingestion and processing pipeline using Docker Compose, AWS services, and monitoring tools. The pipeline should securely transfer data from an SFTP server, process it, store it in an AWS S3 bucket, and provide real-time monitoring of performance and health. This exercise tests your understanding of containerization, cloud services, monitoring, and secure data handling.


	Scenario
	You are tasked with building a data pipeline that ingests log files from an external SFTP server, processes them using a custom Python script, and stores the processed data in an AWS S3 bucket. The pipeline needs to be scalable, monitored, and secure.
	import requests
	import json

	account_id = "1ed--61e"
	url = f"https://api.cloudflare.com/client/v4/accounts/{account_id}/challenges/widgets"
	headers = {
	"X-Auth-Email": "a@gmail.com",
	"X-Auth-Key": "504--1e6" #not API token
	}

	DevOps Exercise: Multi-Service Deployment with Docker Compose
	Objective

	Deploy a scalable, multi-service e-commerce setup using Docker Compose that includes a reverse proxy, product management service, caching, and a database. Your setup should be secure, resilient, and able to handle simulated load. This exercise tests container orchestration, secure configuration management, and performance monitoring.
	Scenario

	You're setting up the backend infrastructure for a product catalog in an e-commerce platform. The platform includes:

	1.API Gateway (Nginx): A reverse proxy that routes requests to the Product Service and caches frequently accessed data.

	<!DOCTYPE html>
	<html>
	<head>
	<title>Payslip - Consolidated</title>
	<style>
	table {
	width: 100%;
	border-collapse: collapse;
	}
	# Define an upstream block for backend services
	upstream api_backend {
	server localhost:6565;
	}

	# HTTP server block to handle requests
	server {
	listen 80;
	server_name ;
	To set up multiple WordPress instances with separate databases using Docker, you'll need to modify the Docker commands to create additional containers for WordPress and MariaDB. Here's how you can set it up for three websites:

	1. Create Docker networks:
	```bash
	docker network create wordpress-network1
	docker network create wordpress-network2
	docker network create wordpress-network3
	```

	2. Create volumes for each WordPress instance and database:
	import os
	from openai import OpenAI

	client = OpenAI(
	api_key = os.getenv("OPENAI_API_KEY"),
	)

	completion = client.chat.completions.create( # Change the method
	model = "gpt-3.5-turbo",
	messages = [ # Change the prompt parameter to messages parameter
	import React, { useEffect } from 'react';
	import * as THREE from 'three';
	import { GLTFLoader } from 'three/examples/jsm/loaders/GLTFLoader';
	import { OrbitControls } from 'three/examples/jsm/controls/OrbitControls';

	function Basic3d() {
	useEffect(() => {
	// Scene
	const scene = new THREE.Scene();
	scene.background = new THREE.Color(0xaaaaaa); // Optional: Change scene background