Microsoft Azure: Planning, Deploying, and Managing the Cloud

ISBN-13: 9781484259573 / Angielski / Miękka / 2020 / 533 str.

Micheleen Harris; Julian Soh; Anthony Puca

Spis treści

PART I  Introducing Microsoft Azure

Ch 1. Microsoft Azure and Cloud Computing

Overview of Microsoft Azure services

Azure concepts

Availability Zones

Security, Compliance, and Privacy primer

Azure licensing and cost management concepts

Subscriptions

Ch 2. Overview of Azure Infrastructure-as-a-Service (IaaS) services

Azure Virtual Machines

Azure Networking

Hybrid data center design concepts (private-public cloud)

Hybrid data center operations and monitoring

IaaS security considerations

Ch 3. Overview of Azure Platform-as-a-Service

Azure Storage Accounts

Azure WebApps

Azure Database Services

Azure PaaS networking services

Azure Machine Learning and Cognitive Services

Azure Load Balancers

Azure Batch

Ch 4. Azure AppDev Services Overview

Azure DevOps and GitHub

Azure IaaS as code

Visual Studio

Containers - AKS

5. Ethical AI, Azure AI, and Machine Learning

Azure for the modern Data Engineers

Azure SQL Database

Azure SQL Managed Instance

Azure SQL Data Warehouse

Azure Cosmos DB

Azure Data Lake Service (gen 2)

Azure Data Factory v2

Cognitive Services

Artificial Intelligence and Machine Learning

 

PART II Planning and adopting Azure

6. Budgeting and cloud economics

Using assessment tools - Microsoft Movere

Understanding Cloud Economics - CapEx vs OpEx

Forecasting and other cost saving features

Autoscaling

Reserved Instances

Service Level Agreement in LRS vs GRS

Azure Cost Mgmt and Billing

7. Designing a Hybrid Data Center

Networking considerations

PaaS Considerations

Identity and Access Management

Security and monitoring

8.Tools, training, and upskilling existing IT personnel

Available and required minimum training

Assembling the toolkit for the Cloud engineer

PowerShell ISE

Visual Studio Code

Azure Storage Explorer

ARM Templates

Hashicorp Terraform

Source control

Common Mistakes

 

PART III Using Azure for Infrastructure-as-a-Service (IaaS)

9. Implementing Azur networking

Designing and implementing Virtual Networks (vNets)

Implementing Site-to-Site VPN

Implementing ExpressRoute

ER Direct

Global Reach

Implementing Network Security Groups

Implementing Security and Monitoring for networks

Network Watcher

Network Perforamance Monitor

Autoscaling

10. Virtual Machines

Creating and managing Virtual Machines

Operating Systems (Windows, Linux)

Gallery Image

Customer Image

VM Disks

Monitoring and health of VM

Securing VM

Automation

Troubleshooting

Improving VM availability

Availability Groups

Disaster Recovery

Azure Site Recovery (ASR)

11. Infrastructure-as-code

Your first Infrastructure-as-code exercise

Deploying VMs with code

Deploying virtual networks with code

Addressing dependencies

Troubleshooting your code

Source control

 

PART IV  Adopting Platform-as-a-Service (PaaS)

12. Azure WebApps

Deploying a WebApp

Publishing to a WebApp

Monitoring and securing WebApps

Azure Security Center for WebApps

Integrating authentication for WebApps

Leveraging Azure Active Directory

B2B and B2C

Multi-factor authentication

Troubleshooting

Use Case: Azure Drupal+MySQL PaaS

13  Network PaaS

Web Application Firewall (WAF)

Load Balancer

Azure DNS

Azure Traffic Manager

Azure Front Door Service

Azure Private Link

Content Delivery Network (CDN)

Azure DDoS Protection

Azure Firewall

Use Case: Implement Azure Front Door Service

14 Azure Storage

Azure Blob Storage

Azure Queues

Azure Files

Use Case: Using Azure Storage Explorer

 

PART V  Azure Data Services and Big Data

15. Azure Database Services

Azure SQL Database

Azure SQL Database

Azure SQL Database Serverless

Azure SQL Database Managed Instance (MI)

Azure SQL Data Warehouse

Azure CosmosDB

Azure Database for MySQL

Azure Tables

Azure Data Lake Services (ADLS)

Determining the right Data Services to use

16 Migrating on-premises databases to Azure

Database Migration Assistant (DMA)

Azure Database Migration Service

17 Data Engineering

Data Engineering and Estate

Extract, transform, and loading data (ETL vs ELT)

Data sharing with Azure Data Share

Azure Data Factory

Pipelines, activities, and datasets

Orchestrating Data Copies

Azure Data Flow (Preview)

Use Case: Copying, combining, and enriching data

PART VI  Azure services for application developers

18. Developing Azure-based applications

Considerations for Cloud-first development practices

Use case: Build and publish an ASP.Net WebApp

Containers and AKS

Use case: Build a .Net Core WebApp in Docker

Monitoring application health and performance in Azure

Designing IoT solutions

19. Azure DevOps

Introducing Azure DevOps

Azure Repos

Azure Pipelines

Azure Boards

Azure Test Plans

Azure Artifacts

GitHub

Use case: Development lifecycle demo using Azure Repo/GitHub, Pipelines, and CI/CD

 

PART VII  Intelligent Cloud - Machine Learning and Artificial Intelligence

20. Azure Cognitive Services (COGS)

Introducing COGS

QnAmaker.ai

Use Case: ChatBots

21. Machine Learning and Deep Learning

Azure Machine Learning overview

Databricks/Spark overview

Use case: Azure Databricks for data scientists

Data preparation

ML Modeling using Azure Auto ML

Build DNN for classification

Parallel and distributed training

IoT and Edge IoT devices

Use case: Real world examples

 

 

O autorze

Julian Soh is a cloud solutions architect with Microsoft, focusing in the areas of artificial intelligence, cognitive services, and advanced analytics. Prior to his current role, Julian worked extensively in major public cloud initiatives, such as SaaS (Microsoft Office 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations.

Marshall Copeland is a security architect focused on kill chain defenses in public cloud deployments using cloud native and third-party cyber solutions. His work focuses on security in hybrid cloud deployments, secure DevOps, and security partner cloud integrations that enhance “blue team hunting” efficiencies.

Anthony Puca is a director of azure apps and infrastructure in Microsoft’s United States Federal Government division. Anthony has been consulting with US federal government departments and agencies on private, public, and hybrid cloud technologies for the last three years.

Micheleen Harris is a technical program manager at Microsoft focusing on AI and machine learning. She has been a developer for over ten years and has a data science focus. She has designed and delivered many courses and given talks at large conferences such as Microsoft /build and ODSC West.

Blurb

Gain the technical and business insight needed to plan, deploy, and manage the services provided by the Microsoft Azure cloud. This second edition focuses on improving operational decision tipping points for the professionals leading DevOps and security teams. This will allow you to make an informed decision concerning the workloads appropriate for your growing business in the Azure public cloud.  

Microsoft Azure starts with an introduction to Azure along with an overview of its architecture services such as IaaS and PaaS. You’ll also take a look into Azure’s data, artificial intelligence, and machine learning services. Moving on, you will cover the planning for and adoption of Azure where you will go through budgeting, cloud economics, and designing a hybrid data center. Along the way, you will work with web apps, network PaaS, virtual machines, and much more. 

The final section of the book starts with Azure data services and big data with an in-depth discussion of Azure SQL Database, CosmosDB, Azure Data Lakes, and MySQL. You will further see how to migrate on-premises databases to Azure and use data engineering. Next, you will discover the various Azure services for application developers, including Azure DevOps and ASP.NET web apps. Finally, you will go through the machine learning and AI tools in Azure, including Azure Cognitive Services.

You will:

• Apply design guidance and best practices using Microsoft Azure to achieve business growth
• Create and manage virtual machines
• Work with AI frameworks to process and analyze data to support business decisions and increase revenue Deploy, publish, and monitor a web app