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Hey there,

As cloud professionals, we are always looking for ways to improve our skills and build solutions that are scalable, secure, and efficient. While regular news and updates keep us informed, sometimes it's good to take a deep dive into topics that matter.

That’s why we’re bringing you this special issue of CloudPro, where we explore three carefully selected books that provide practical, hands-on learning experiences.

The first book, Data Engineering with AWS Cookbook, provides hands-on solutions for optimizing ETL workflows using AWS services. We've included a practical excerpt on Building an ETL Pipeline with AWS Glue and PySpark, demonstrating how to process and transform large-scale data efficiently in the cloud.

The second book, Mastering Windows 365, explores automation strategies for deploying and managing Windows Cloud PCs. We’ll dive into an in-depth excerpt on Automating Windows 365 Deployment with PowerShell, covering how to streamline provisioning and policy management for enterprise environments.

The third book, Platform Engineering for Architects, focuses on securing and optimizing Kubernetes environments for multi-tenant workloads. In this issue, we present a highly technical excerpt on Implementing Kubernetes Role-Based Access Control (RBAC), showcasing how to enforce fine-grained access policies for secure cluster operations.

If you’re serious about learning by doing, this issue is for you. Dive in and explore!

Data Engineering with AWS Cookbook

Data is the backbone of modern cloud applications, and AWS provides a rich set of tools for data ingestion, transformation, and orchestration. This cookbook takes a hands-on, solution-driven approach to solving real-world data engineering challenges.

Deep Dive: ETL Pipeline with AWS Glue and PySpark

A common challenge in data engineering is efficiently handling large-scale ETL workloads. Below is an AWS Glue PySpark job for processing and transforming S3 data:

import sys
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from awsglue.context import GlueContext
from awsglue.job import Job
from pyspark.context import SparkContext

# Initialize Glue context
sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)

# Read data from S3
s3_data = glueContext.create_dynamic_frame.from_options(
"s3", {'paths': ["s3://my-bucket/raw-data"]}, format="json")

# Transform data
transformed_data = ApplyMapping.apply(
frame=s3_data,
mappings=[("name", "string", "full_name", "string"),
("age", "int", "user_age", "int")]
)

# Write data back to S3
output_path = "s3://my-bucket/processed-data"
glueContext.write_dynamic_frame.from_options(
frame=transformed_data, "s3", {'path': output_path}, format="parquet"
)

job.commit()

This job reads JSON data from S3, transforms it using Glue, and stores the results in Parquet format for optimized analytics. AWS Glue automates infrastructure management, making it ideal for serverless ETL.

Mastering Windows 365

Windows 365 is redefining the future of cloud-based computing by offering a seamless, secure, and scalable way to deploy virtual desktops. This book provides deep insights into architecture, deployment, security, and optimization strategies for Windows 365 environments.

Deep Dive: Automating Windows 365 Deployment with PowerShell

Automating deployment is critical for managing Windows 365 at scale. Below is a PowerShell script to create and configure a Windows 365 Cloud PC environment:

# Install Windows 365 Module
Install-Module -Name Windows365 -Scope CurrentUser -Force

# Connect to Microsoft Graph API
Connect-MgGraph -Scopes "CloudPC.ReadWrite.All"

# Create a Windows 365 provisioning policy
$policy = New-MgCloudPcProvisioningPolicy -DisplayName "CloudPC-Policy" `
-ImageId "Win10-Enterprise" -UserSettings "Standard"

# Assign users to Cloud PCs
$users = Get-MgUser -Filter "jobTitle eq 'Developer'"
foreach ($user in $users) {
New-MgCloudPcUserSetting -UserId $user.Id -ProvisioningPolicyId $policy.Id
}

This script automates the creation of Windows 365 instances for a team of developers based on their job titles, ensuring consistency and security across deployments.

Platform Engineering for Architects

As cloud environments grow increasingly complex, platform engineering has emerged as the next evolution in DevOps. This book provides a blueprint for building scalable, secure, and developer-friendly cloud platforms.

Deep Dive: Kubernetes Role-Based Access Control (RBAC)

Implementing fine-grained RBAC in Kubernetes is critical for security and multi-tenant environments. Below is an example YAML configuration for setting up a read-only role in a monitoring namespace:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
namespace: monitoring
name: read-only-role
rules:
- apiGroups: [""]
resources: ["pods", "services", "configmaps"]
verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: RoleBinding
metadata:
name: read-only-binding
namespace: monitoring
subjects:
- kind: ServiceAccount
name: monitoring-sa
namespace: monitoring
roleRef:
kind: Role
name: read-only-role
apiGroup: rbac.authorization.k8s.io

This setup grants read-only access to essential resources in the monitoring namespace, ensuring secure, least-privilege access for observability tooling.

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Thanks for reading and have a great day!