Specialize in Cloud Computing through a flexible degree, compatible with your daily responsibilities”

The ability to store, process and manage data in the cloud has transformed the way businesses operate, enabling process automation, scalability and cost reduction. These benefits have given impetus to the development of the Cloud and its application in all sectors and organizations, regardless of their size. 

In view of this reality, the profile of the computer scientist is of great importance and in recent years has become one of the most in-demand. A favorable scenario that requires specialized professionals who are up to date with the latest trends. This is the origin of this 24-month Grand Master's Degree in Cloud Computing. 

This is an advanced program that will take the graduate on an intensive academic journey through the programming of Cloud Computing architectures, Native Cloud application programming, and container orchestration with Kubernetes and Docker. This degree also covers topics such as storage in Cloud Azure, integration of cloud services, and transformation of IT infrastructures towards Cloud Computing.

Furthermore, the numerous teaching materials will enable students to learn more about security, governance and cybersecurity in Cloud infrastructures, as well as monitoring and backup in a much more agile way. A unique teaching that reduces the long hours of study and memorization, thanks to the Relearning, method, which is another attraction to take this program. 

In this way, through an eminently online and totally flexible academic option, the computer scientist will obtain the knowledge he/she needs to grow in the technology industry. In order to access the content of this degree program at any time of the day, all you need is an electronic device with an Internet connection. An ideal opportunity for quality training compatible with daily responsibilities.

A unique academic option, whose Relearning system will allow you to learn easily and reduce the long hours of study”

This Advanced master’s degree in Cloud Computing contains the most complete and up-to-date program on the market. The most important features include:

• The development of practical cases presented by rendering experts
• The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Special emphasis on innovative methodologies in the realization of Cloud Computing Project
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

Do you want to be at the forefront of digital transformation? Enroll now in this Grand Master in Cloud Computing and learn how to create innovative solutions for the companies of the future”

Its teaching staff includes professionals from the Cloud Computing, who bring to this program the experience of their work, as well as recognized specialists from reference societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide an immersive learning experience designed to prepare for real-life situations.

This program is designed around Problem-Based Learning, whereby the student must try to solve the different professional practice situations that arise throughout the program. For this purpose, the professional will be assisted by an innovative interactive video system created by renowned and experienced experts.

With this degree you will become an expert in programming cloud architectures with the most widely used technologies, such as Azure, AWS and Google Cloud"

You will learn how to deploy containers with Kubernetes and Docker, key technologies for the implementation of cloud solutions"

The syllabus of this program has been designed to provide students with the most current knowledge about Cloud Computing. In order to achieve this goal successfully, TECH provides the graduates with pedagogical tools based on video summaries of each topic, detailed videos, case studies and specialized literature that will allow them to further extend this advanced syllabus. In addition, these materials will be available in the Learning Resources Library 24 hours a day, 7 days a week.

Access the Virtual Library of this program 24 hours a day, from any digital device with internet connection”

Module 1. Cloud Programming Azure, AWS and Google Cloud Services

1.1. Cloud Cloud Services and Technologies

1.1.1. Cloud Services and Technologies
1.1.2. Cloud Terminology
1.1.3. Reference Cloud Providers

1.2. Cloud Computing

1.2.1. Cloud Computing
1.2.2. Cloud Computing Ecosystem
1.2.3. Types of Cloud Computing

1.3. Cloud Service Models

1.3.1. IaaS Infrastructure as a Service
1.3.2. SaaS Software as a Service
1.3.3. PaaS Platform as a Service

1.4. Cloud Computing Technologies

1.4.1. Virtualization Systems
1.4.2. Service-Oriented Architecture (SOA)
1.4.3. GRID Computing

1.5. Architecture Cloud Computing

1.5.1. Architecture Cloud Computing
1.5.2. Networks Types in Cloud Computing
1.5.3. Cloud Computing Security

1.6. Public Cloud

1.6.1. Public Cloud
1.6.2. Public Cloud Architecture and Costs
1.6.3. Public Cloud. Typology

1.7. Public Cloud

1.7.1. Public Cloud
1.7.2. Architecture and Costs
1.7.3. Public Cloud. Typology

1.8. Hybrid Cloud

1.8.1. Hybrid Cloud
1.8.2. Architecture and Costs
1.8.3. Hybrid Cloud Typology

1.9. Cloud Providers

1.9.1. Amazon Web Services
1.9.2. Azure
1.9.3. Google

1.10. Cloud Security

1.10.1. Infrastructure Security
1.10.2. Operating System and Network Security
1.10.3. Cloud Risk Mitigation

Module 2. Architecture Programming in Cloud Computing

2.1. Cloud Architecture for a University Network Cloud Provider Selection Practical Example

2.1.1. Cloud Architecture Approach for a University Network According to Cloud Provider
2.1.2. Cloud Architecture Components
2.1.3. Analysis of Cloud Solutions According to Proposed Architecture

2.2. Economic Estimation of the Project for the Creation of a University Network Financing

2.2.1. Cloud Provider Selection
2.2.2. Economical Estimation According to Components
2.2.3. Project Financing

2.3. Estimation of Human Resources of the Project Composition of a Software Team

2.3.1. Composition of the Software Development Team
2.3.2. Roles in a Development Team Typology
2.3.3. Assessment of the Economic Estimation of the Project

2.4. Execution Schedule and Project Documentation

2.4.1. Agile Project Schedule
2.4.2. Project Feasibility Documentation
2.4.3. Documentation to Be Provided for Project Execution

2.5. Legal Implications of a Project

2.5.1. Legal Implications of a Project
2.5.2. Data Protection Policy

2.5.2.1. GDPR General Data Protection Regulation

2.5.3. Responsibility of the Integrating Company

2.6. Design and Creation of a Cloud Blockchain Network for the Proposed Architecture

2.6.1. Blockchain – Hyperledger Fabric
2.6.2. Hyperledger Fabric Basics
2.6.3. Design of an International University Hyperledger Fabric Network

2.7. Proposed Architecture Expansion Approach

2.7.1. Creation of the Proposed Architecture with Blockchain
2.7.2. Proposed Architecture Expansion
2.7.3. Configuration of a High Availability Architecture

2.8. Administration of the Proposed Cloud Architecture

2.8.1. Adding a New Participant to the Initial Proposed Architecture
2.8.2. Administration of the Cloud Architecture
2.8.3. Project Logic Management – Smart Contracts

2.9. Administration and Management of Specific Components in the Proposed Cloud Architecture

2.9.1. Management of Network Certificates
2.9.2. Security Management of Various Components: CouchDB
2.9.3. Blockchain Network Nodes Management

2.10. Modification of an Initial Basic Installation in the Creation of a Blockchain Network

2.10.1. Adding a Node to the Blockchain Network
2.10.2. Addition of Extra Data Persistence
2.10.3. Smart Contracts Management
2.10.4. Addition of a New University to the Existing Network

Module 3. Cloud Azure Storage

3.1. MV Installation in Azure

3.1.1. Creation Commands
3.1.2. Visualization Commands
3.1.3. Modification Commands

3.2. Azure Blobs

3.2.1. Types of Blobs
3.2.2. Container
3.2.3. Azcopy
3.2.4. Reversible Blob Suppression

3.3. Managed Disk and Storage in Azure

3.3.1. Managed Disk
3.3.2. Security/Safety
3.3.3. Cold Storage
3.3.4. Replication

3.3.4.1. Local Redundancy
3.3.4.2. Redundancy in a Zone
3.3.4.3. Geo-Redundant

3.4. Azure Tables, Queues, Files

3.4.1. Tables
3.4.2. Queues
3.4.3. Files

3.5. Azure Encryption and Security

3.5.1. Storage Service Encryption (SSE)
3.5.2. Access Codes

3.5.2.1. Shared Access Signature
3.5.2.2. Container-Level Access Policies
3.5.2.3. Access Signature at Blob Level

3.5.3. Azure AD Authentication

3.6. Azure Virtual Network

3.6.1. Subnetting and Matching
3.6.2. Vnet to Vnet
3.6.3. Private Link
3.6.4. High Availability

3.7. Types of Azure Connections

3.7.1. Azure Application Gateway
3.7.2. Site-to-Site VPN
3.7.3. Point-to-Site VPN
3.7.4. ExpressRoute

3.8. Azure Resources

3.8.1. Blocking Resources
3.8.2. Resource Movement
3.8.3. Removal of Resources

3.9. Azure Backup

3.9.1. Recovery Services
3.9.2. Azure Agent Backup
3.9.3. Azure Backup Server

3.10. Solutions Development

3.10.1. Compression, Deduplication, Replication
3.10.2. Recovery Services
3.10.3. Disaster Recovery Plan

Module 4. Cloud Environments: Security/Safety

4.1. Cloud Environments: Security/Safety

4.1.1. Cloud Environments, Security

4.1.1.1. Cloud Security
4.1.1.2. Security Position

4.2. Cloud Shared Security Management Model

4.2.1. Security Elements Managed by Vendor
4.2.2. Elements Managed by Customer
4.2.3. Security Strategy

4.3. Cloud Prevention Mecanisms

4.3.1. Authentication Management Systems
4.3.2. Authorization Management System Access Policies
4.3.3. Key Management Systems

4.4. Cloud Infrastructure Data Security

4.4.1. Securing Storage Systems:

4.4.1.1. Block
4.4.1.2. Object Storage
4.4.1.3. File Systems

4.4.2. Protection of Database Systems
4.4.3. Securing Data in Transit

4.5. Cloud Infrastructure Protection

4.5.1. Secure Network Design and Implementation
4.5.2. Security in Computing Resources
4.5.3. Tools and Resources for Infrastructure Protection

4.6. Application Risks and Vulnerabilities

4.6.1. Application Development Risks
4.6.2. Critical Safety Risks
4.6.3. Vulnerabilities in Software Development

4.7. Application Defenses against Attacks

4.7.1. Application Development Design
4.7.2. Securitization through Verification and Testing
4.7.3. Secure Programming Practices

4.8. DevOps Environment Security

4.8.1. Security in Virtualized and Container Environments
4.8.2. Security in Development and Operations (DevSecOps)
4.8.3. Best Security Practices in Containerized Production Environments

4.9. Security in Public Clouds

4.9.1. AWS
4.9.2. Azure
4.9.3. Oracle Cloud

4.10. Security Regulations, Governance and Compliance

4.10.1. Security Compliance
4.10.2. Risk Management
4.10.3. Processes in Organizations

Module 5. Container Orchestration: Kubernetes and Docker

5.1. Basis of Application Architectures

5.1.1. Current Application Models
5.1.2. Application Execution Platforms
5.1.3. Container Technologies

5.2. Docker Architecture

5.2.1. Docker Architecture
5.2.2. Docker Architecture Installation
5.2.3. Commands Local Project

5.3. Docker Architecture Storage Management

5.3.1. Image and Register Management
5.3.2. Docker Networks
5.3.3. Storage Management

5.4. Advanced Docker Architecture

5.4.1. Docker Compose
5.4.2. Docker in Organization
5.4.3. Docker Adoption Example

5.5. Kubernetes Architecture

5.5.1. Kubernetes Architecture
5.5.2. Kubernetes Deployment Elements
5.5.3. Distributions and Managed Solutions
5.5.4. Installation and Environment

5.6. Kubernetes Architecture Kubernetes Development

5.6.1. Tools for K8s Development
5.6.2. Imperative vs. Declarative Mode
5.6.3. Application Deployment and Exposure

5.7. Kubernetes in Enterprise Environments

5.7.1. Data Persistence
5.7.2. High Availability, Scaling and Networking
5.7.3. Kubernetes Security
5.7.4. Kubernetes Management and Monitoring

5.8. K8s Distributions

5.8.1. Deployment Environment Comparison
5.8.2. Deployment on GKE, AKS, EKS or OKE
5.8.3. On Premise Deployment

5.9. Rancher and Openshift

5.9.1. Rancher
5.9.2. Openshift
5.9.3. Openshift: Configuration and Application Deployment

5.10. Kubernetes Architecture and Containers Updates

5.10.1. Open Application Model
5.10.2. Tools for Deployment Management in Kubernetes Environments
5.10.3. References to Other Projects and Trends

Module 6. Native Cloud Application Programming

6.1. Cloud Native Technologies

6.1.1. Cloud Native Technologies
6.1.2. Cloud Native Computing Foundation
6.1.3. Cloud-Native Development Tools

6.2. Cloud-Native Application Architecture

6.2.1. Cloud-Native Application Design
6.2.2. Cloud-Native Architecture Components
6.2.3. Legacy Application Modernization

6.3. Containerization

6.3.1. Container-Oriented Development
6.3.2. Development with Microservices
6.3.3. Tools for Teamwork

6.4. DevOps and Continuous Integration and Deployments

6.4.1. Continuous Integration and Deployments: CI/CD
6.4.2. Tools Ecosystem for CI/CD
6.4.3. Creating a CI/CD Environment

6.5. Observability and Platform Analysis

6.5.1. Cloud-Native Application Observability
6.5.2. Tools for Monitoring, Logging  and Tracing
6.5.3. Implementation of an Observability and Analysis Environment

6.6. Data Management in Cloud-Native Applications

6.6.1. Cloud-Native Database
6.6.2. Data Management Patterns
6.6.3. Technologies to Implement Data Management Patterns

6.7. Communications in Cloud-Native Applications

6.7.1. Synchronous and Asynchronous Communications
6.7.2. Technologies for Synchronous Communications Patterns
6.7.3. Technologies for Asynchronous Communications Patterns

6.8. Resilience, Security and Performance in Cloud-Native Applications

6.8.1. Application Resilience
6.8.2. Secure Development in Cloud-Native Applications
6.8.3. Application Performance and Scalability

6.9. Serverless

6.9.1. Cloud Native Serverless
6.9.2. Serverless Platforms
6.9.3. Use Cases for Serverless Development

6.10. Deployment Platforms

6.10.1. Cloud-Native Development Environments
6.10.2. Orquestration Platforms Comparison
6.10.3. Infrastructure Automation

Module 7. Cloud Programming Data Governance

7.1. Data Management

7.1.1. Data Management
7.1.2. Data Handling Ethics

7.2. Data Governance

7.2.1. Classification. Access Control
7.2.2. Data Processing Regulation
7.2.3. Data Governance Value

7.3. Data Governance Data Science

7.3.1. Lineage
7.3.2. Metadata
7.3.3. Data Catalog Business Glossary

7.4. User and Processes in Data Governance

7.4.1. Users

7.4.1.1. Roles and Responsibilities

7.4.2. Processes

7.4.2.1. Data Enrichment

7.5. Data Life Cycle in the Enterprise

7.5.1. Data Creation
7.5.2. Data Processing
7.5.3. Data Storage
7.5.4. Data Use
7.5.5. Data Destruction

7.6. Data Quality

7.6.1. Quality in Data Governance
7.6.2. Data Quality in Analytics
7.6.3. Data Quality Techniques

7.7. Data Governance in Transit

7.7.1. Data Governance in Transit

7.7.1.1. Lineage

7.7.2. The Forth Dimension

7.8. Data Protection

7.8.1. Access Levels
7.8.2. Classification
7.8.3. Compliance Regulations

7.9. Data Governance Monitoring and Measurement

7.9.1. Data Governance Monitoring and Measurement
7.9.2. Lineage Monitoring
7.9.3. Data Quality Monitoring

7.10. Data Governance Tools

7.10.1. Talend
7.10.2. Collibra
7.10.3. IT specialist

Module 8. Real-Time Cloud Programming. Streaming

8.1. Processing and Structuring of Streaming Information

8.1.1. Data Collection, Structuring, Processing, Analysis, and Interpretation Process
8.1.2. Streaming Data Processing Techniques
8.1.3. Streaming Processing
8.1.4. StreamingProcessing Use Cases

8.2. Statistics for Understanding Streaming Data Flows

8.2.1. Descriptive Statistics
8.2.2. Probability Calculation
8.2.3. Inference

8.3. Programmng with Python

8.3.1. Typology, Conditionals, Functions and Loops
8.3.2. Numpy, Matplotlib, Dataframes, Csv Files and Json Formats
8.3.3. Sequences: Lists, Loops, Files and Dictionaries
8.3.4. Mutability, Exceptions and Higher-Order Functions

8.4. R Programming

8.4.1. R Programming
8.4.2. Vector and Factors
8.4.3. Matrix and Array
8.4.4. Lists and Data Frame
8.4.5. Functions

8.5. SQL Database for Streaming Data Processing

8.5.1. SQL Databases
8.5.2. Entity-Relationship Model
8.5.3. Relational Model
8.5.4. SQL

8.6. Non-SQL Database for Streaming Data Processing

8.6.1. Non-SQL Databases
8.6.2. MongoDB
8.6.3. MongoDB Architecture
8.6.4. CRUD Operations
8.6.5. Find, Projections, Index Aggregation and Cursors
8.6.6. Data Model

8.7. Data Mining and Predictive Modeling

8.7.1. Multivariate Analysis
8.7.2. Dimension Reduction Techniques
8.7.3. Cluster Analysis
8.7.4. Sets

8.8. Maching learning for Streaming Data Processing

8.8.1. Maching learning and Advanced Predictive Modeling
8.8.2. Neural Networks
8.8.3. Deep Learning
8.8.4. Bagging and Random Forest
8.8.5. Gradient Bosting
8.8.6. SVM
8.8.7. Assembly Methods

8.9. Streaming Data Processing Technologies

8.9.1. Spark Streaming
8.9.2. Kafka Streaming
8.9.3. Flink Streaming

8.10. Apache Spark Streaming

8.10.1. Apache Spark Streaming
8.10.2. Spark Components
8.10.3. Spark Architecture
8.10.4. RDD
8.10.5. SPARK SQL
8.10.6. Jobs, Stages and  Tasks

Module 9. Cloud Integration with Web Services: Technologies and Protocols

9.1. Web Standards and Protocols

9.1.1. Web and Web 2.0
9.1.2. Client-Server Architecture
9.1.3. Communication Protocols and Standards

9.2. Web Services

9.2.1. Web Services
9.2.2. Communication Layers and Mecanisms
9.2.3. Service Architectures

9.3. Service Oriented Architectures

9.3.1. ServiceOriented Architecture (SOA)
9.3.2. Web Service Design
9.3.3. SOAP and REST

9.4. SOAP Service Oriented Arquitecture

9.4.1. Structure and Message Passing
9.4.2. Web Service Description Language (WSDL)
9.4.3. Client Implementation and SOAP Servers

9.5. REST Architecture

9.5.1. REST Architectures and RESTful Web Services
9.5.2. HTTP Verbs: Semantics and Purposes
9.5.3. Swagger
9.5.4. Client Implementation and REST Servers

9.6. Microservices-Based Architectures

9.6.1. Monolithic Architectural Approach vs. Use of Microservices
9.6.2. Microservices-Based Architectures
9.6.3. Communication Flows with the Use of Microservices

9.7. Invoking APIs from the Client Side

9.7.1. Types of Web Clients
9.7.2. Development Tools for Web Services Processing
9.7.3. Cross-Origin Resources (CORS)

9.8. API Invocation Security

9.8.1. Web Services Security
9.8.2. Authentication and Authorization
9.8.3. Authentication Methods Based on the Degree of Security

9.9. Cloud Provider Application Integration

9.9.1. Cloud Computing Suppliers
9.9.2. Platform Services
9.9.3. Services Oriented to the Implementation/Consumption of Web Services

9.10. Implementation of Bots and Wizards

9.10.1. Use of Bots
9.10.2. Use of the Bots Web Service
9.10.3. Implementation of Chatbots and Web Assistants

Module 10. Cloud Programming Project Management and Product Verification

10.1. Waterfall Methodologies

10.1.1. Classification of Methodologies
10.1.2. Waterfall Model Waterfall
10.1.3. Strength and Weakness
10.1.4. Model Comparison Waterfall vs. AGILE

10.2. Agile Methodology

10.2.1. Agile Methodology
10.2.2. The Agile Manifesto
10.2.3. Use of Agile

10.3. Scrum Methodology

10.3.1. Scrum Methodology

10.3.1.1. Use of Scrum

10.3.2. Scrum Events
10.3.3. Scrum Artifacts
10.3.4. Scrum Guide

10.4. Agile Inception Desk

10.4.1. Agile Inception Desk
10.4.2. Inception Desk Phases

10.5. Impact Mapping Technique

10.5.1. Impact Mapping
10.5.2. Use of Impact Mappig
10.5.3. Impact Mapping Structure

10.6. User Stories

10.6.1. User Stories
10.6.2. Writing User Stories
10.6.3. User Story Hierarchy
10.6.4. Use Story Mapping

10.7. Test Qa Manual

10.7.1. Testing Manual
10.7.2. Validation and Verification Differences
10.7.3. Manual Tests Typology
10.7.4. UAT User Acceptance Testing
10.7.5. UAT and Alpha & Beta Testing
10.7.6. Software Quality

10.8. Automatic Tests

10.8.1. Automatic Tests
10.8.2. Manual Tests vs Automatic
10.8.3. The Impact of the Automatic Test
10.8.4. The Result of Applying Automation
10.8.5. The Quality Wheel

10.9. Functional and Non-Functional Testing

10.9.1. Functional and Non-Functional Testing
10.9.2. Functional Tests

10.9.2.1. Unit Tests
10.9.2.2. Integration Tests
10.9.2.3. Regression Testing
10.9.2.4. Smoke Tests
10.9.2.5. Mono Tests
10.9.2.6. Sanitation Tests

10.9.3. Non-Functional Tests

10.9.3.1. Load Testing
10.9.3.2. Performance Testing
10.9.3.3. Security Tests
10.9.3.4. Configuration Tests
10.9.3.5. Stress Tests

10.10. Verification Methods and Tools

10.10.1. Heat Map
10.10.2. Eye Tracking
10.10.3. Scroll Maps
10.10.4. Movement Maps
10.10.5. Confetti Maps
10.10.6. Test A/B
10.10.7. Blue & Green Deployment Method
10.10.8. Canary Release Method
10.10.9. Tool Selection
10.10.10. Analytical Tools

Module 11. Transformation of IT Infrastructures Cloud Computing

11.1. Cloud Computing Cloud Computing Adoption

11.1.1. Computing
11.1.2. Cloud Computing Adoption
11.1.3. Types of Cloud Computing

11.2. Cloud Computing Adoption. Adoption Factors

11.2.1. Adoption Factors of Cloud Infrastructures
11.2.2. Uses and Services
11.2.3. Evolution

11.3. Cloud Computing Infrastructures

11.3.1. Cloud Computing Infrastructures
11.3.2. Types of Infrastructures (IaaS, PaaS, SaaS)
11.3.3. Types of Implementation (private, public, hybrid)
11.3.4. Elements (hardware, storage, network)

11.4. Cloud Computing Infrastructure: Operation

11.4.1. Virtualisation
11.4.2. Automation
11.4.3. Management

11.5. Cloud Computing Ecosystem

11.5.1. Observability and Analysis
11.5.2. Procurement
11.5.3. Orchestration and Management
11.5.4. Cloud Platforms

11.6. Services Management in Cloud Infrastructures

11.6.1. Service Orientation
11.6.2. Standard and Ecosystem
11.6.3. Types of Services

11.7. Cloud Infrastructure Management Automation

11.7.1. Ecosystem
11.7.2. DevOps Culture
11.7.3. Infrastructure as Code (Terraform, Ansible, Github, Jenkins)

11.8. Security in Cloud Infrastructures

11.8.1. Ecosystem
11.8.2. DevSecOps Culture
11.8.3. Data Science

11.9. Preparation of the Cloud Infrastructure Management Environment

11.9.1. Data Science
11.9.2. Preparation of the Environment
11.9.3. First Steps

11.10. Cloud Infrastructures Future and Evolution

11.10.1. Cloud Infrastructures Challenges
11.10.2. Evolution of Cloud Infrastructures
11.10.3. Challenges in Security and Complaince

Module 12. Infrastructure as a Service (IaaS)

12.1. Cloud Computing Abstraction Layers and Their Management

12.1.1. Abstraction Core Concepts
12.1.2. Services Models
12.1.3. Management of Cloud Services. Benefits

12.2. Construction of Architecture. Core Decisions

12.2.1. HDDC and SDDC. Hypercompetition
12.2.2. Market
12.2.3. Working Model amd Professional Profiles Changes

12.2.3.1. Cloudbroker Figure

12.3. Digital Transformation and Cloud Infrastructures

12.3.1. Cloud Work Demo
12.3.2. The Role of the Navigator as Tool
12.3.3. New Device Concept
12.3.4. Advanced Architectures and the Role of the CIO

12.4. Agile Management in Cloud Infrastructures

12.4.1. Life Cycle of New Services and Competitiveness
12.4.2. Development Methodology of Apps and Microservices
12.4.3. Relationship between Development and IT Transactions

12.4.3.1. Use of Cloud as Support

12.5. Cloud Computing Resources I. Identity, Storage and Domain Management

12.5.1. Identity and Access Management
12.5.2. Secure Data Storage, Flexible File and Database Storage
12.5.3. Domain Management

12.6. Cloud Computing Resources II. Network, Infrastructure and Monitoring Resources

12.6.1. Private Virtual Network
12.6.2. Cloud Computing Capabilities
12.6.3. Monitoring

12.7. Cloud Computing Resources III. Automation

12.7.1. Serverless Code Execution
12.7.2. Message Queuing
12.7.3. Workflow Services

12.8. Cloud Computing Resources IV. Other Services

12.8.1. Notification Queuing
12.8.2. Streaming  Services and Transcoding Technologies
12.8.3. Turnkey Solution to Publish APIs for External and Internal Consumers

12.9. Cloud V Computing Resources. Data-Centric Services

12.9.1. Data Analytics Platforms and Automation of IT Manual Task
12.9.2. Data Migration
12.9.3. Hybrid Cloud

12.10. LaaS Services Practice Lab

12.10.1. Exercise 1
12.10.2. Exercise 2
12.10.3. Exercise 3

Module 13. Storage and Databases in Cloud Infrastructures

13.1. Cloud Storage Infraestucture

13.1.1. Cloud Storage Fundamentals
13.1.2. Cloud Storage Advantages
13.1.3. Operation

13.2. Types of Cloud Storage

13.2.1. SaaS
13.2.2. IaaS

13.3. Cloud Storage Use Cases

13.3.1. Data Analysis
13.3.2. Backup and Archiving
13.3.3. Software Development

13.4. Cloud Storage Security

13.4.1. Security in the Transport Layer
13.4.2. Storage Security
13.4.3. Storage Encryption

13.5. Cloud Storage Analysis

13.5.1. Profitability
13.5.2. Agility and Scalability
13.5.3. Administration

13.6. Infrastructure of Cloud Database

13.6.1. Fundamentals of Databases
13.6.2. Analysis of Databases
13.6.3. Cloud Database Classification

13.7. Infrastructure of Cloud Database

13.7.1. Relational Databases
13.7.2. NO-SQL Databases
13.7.3. OnCloud Databases

13.8. The use cases of Infrastructure of Cloud Database

13.8.1. Data Storage
13.8.2. Data Analysis. IA.ML
13.8.3. Big Data

13.9. Safety Infrastructure of Cloud Database

13.9.1. Access Control ACL, IAM, SG
13.9.2. Data Encryption
13.9.3. Audits

13.10. Infrastructure of Cloud Database

13.10.1. Backups of Databases
13.10.2. Migration of Databases
13.10.3. Optimization of Databases

Module 14. Network DevOps and Network Architectures in Cloud infrastructures

14.1. Network DevOps (NetOps)

14.1.1. Network DevOps (NetOps)
14.1.2. NetOps Methodology
14.1.3. NetOps Benefits

14.2. Network DevOps Fundamentals

14.2.1. Networking Fundadamentals
14.2.2. OSI TCP/IP Model, CIDR and Subnetting
14.2.3. Main Protocols
14.2.4. HTTP Responses

14.3. Tools and software for Network DevOps

14.3.1. Network Layer Tools
14.3.2. Tools in Application Layer
14.3.3. DNS Tools

14.4. Networking in Cloud Environments: Internal Network Services

14.4.1. VLAN Virtual Networks
14.4.2. Subnetworks
14.4.3. Routing Tables
14.4.4. Availability Zones

14.5. Networking in Cloud Environments: Border Network Services

14.5.1. Internet Gateway
14.5.2. NAT Gateway
14.5.3. Load Balancing

14.6. Networking in Cloud Environments: DNS

14.6.1. DNS Fundamentals
14.6.2. Cloud DNS Services
14.6.3. HA / LB via DNS

14.7. Hybrid / Multitenant Network Connectivity

14.7.1. VPN Site to Site
14.7.2. VPC Peering
14.7.3. Transit Gateway / VPC Peering

14.8. Content Delivery Network Services

14.8.1. Content Delivery Services
14.8.2. AWS CLoudFront
14.8.3. Others CDNs

14.9. Security in Cloud Networks

14.9.1. Security Principles in Networks
14.9.2. Layer 3 and 4 Protection
14.9.3. Layer 7 Protection

14.10. Network Monitoring and Auditing

14.10.1. Monitoring and Auditing
14.10.2. Flow Logs
14.10.3. Monitoring Services: CloudWatch

Module 15. Government in Cloud Infrastructures

15.1. Compliance with in Cloud Environments

15.1.1. Shared Responsibilities Model
15.1.2. Laws, Regulations and Contracts
15.1.3. Audits

15.2. The CISO in Cloud Governance

15.2.1. Organizational Framework. Figures of the CISO in the Organisation
15.2.2. Relationship of the CISO with data processing areas
15.2.3. GRC Strategy against Shadow IT

15.3. Cloud Governance Standard

15.3.1. Previous Assessments
15.3.2. Cloud Service Provider Compliance
15.3.3. Personnel Obligations

15.4. Privacy in Cloud Environments

15.4.1. Consumer and User Relationship with Privacy
15.4.2. Privacy in the Americas, Asia Pacific, Middle East and Africa
15.4.3. Privacy in the European context

15.5. Approvals and regulatory frameworks in Cloud Environments

15.5.1. American Approvals and Frameworks 
15.5.2. Asian Approvals and Frameworks 
15.5.3. Approvals and Frameworks  in Europe

15.6. Certifications and accreditations in Cloud Environments

15.6.1. America and Asia Pacific
15.6.2. Europe, Middle East and Africa
15.6.3. Global

15.7.  Laws / Regulations in Cloud Environments

15.7.1. CLOUD Act,  HIPAA, IRS 1075
15.7.2. ITAR, SEC Rule 17a-4(f), VPAT/Section 508
15.7.3. European Regulation

15.8. Cost control and billing in Cloud Governance

15.8.1. Pay-Per-Use Models Costs
15.8.2. CFO Figure and FinOps Profiles
15.8.3. Expense Control 

15.9. Tools in Cloud Governance

15.9.1. OvalEdge
15.9.2. ManageEngine ADAudit Plus
15.9.3. Erwin Data Governance

15.10. Corporate Governance

15.10.1. Code of Conduct
15.10.2. Complaints Channel
15.10.3. Due Diligence

Module 16. Cybersecurity in Cloud Infrastructures

16.1. Risk in Cloud Environments

16.1.1. Cybersecurity Strategies
16.1.2. Risk-Based Approach
16.1.3. Categorization of risks in Cloud environments

16.2. Security Frameworks in Cloud Environments

16.2.1. Cybersecurity Frameworks and Standards
16.2.2. Technical Cybersecurity Frameworks
16.2.3. Organizational Cybersecurity Frameworks

16.3. Threats Modeling in Cloud Environments

16.3.1. Threat Modeling Process
16.3.2. Phases of Threat Management
16.3.3. STRIDE

16.4. Cybersecurity Data Science at Code Level

16.4.1. Classification of tools
16.4.2. Integrations
16.4.3. Examples of use

16.5. Cybersecurity Controls Integrations in Cloud Environments

16.5.1. Security in Processes
16.5.2. Safety Controls in the Different Phases
16.5.3. Examples of Integrations

16.6.  ZAP Proxy Tool

16.6.1. ZAP Proxy
16.6.2. ZAP Proxy Features
16.6.3. ZAP Proxy Automation

16.7. Automated Vulnerability Scanning in Cloud Environments

16.7.1. Persistent and Automated Vulnerability Scanning
16.7.2. OpenVAS
16.7.3. Vulnerability Analysis in Cloud Environments

16.8. Firewalls in Cloud Environments

16.8.1. Types of Firewalls
16.8.2. Importance of Indicators
16.8.3. OnPremise firewalls and Cloud firewalls

16.9. Security Transport in Cloud Environments

16.9.1. SSL/TLS and Certificates
16.9.2. SSL Audits
16.9.3. The Automation for Certificates

16.10. SIEM in Cloud Environments

16.10.1. SIEM as a Security Core
16.10.2. Cyberintelligence
16.10.3. Examples of SIEM Systems

Module 17. Services Adoption in Cloud Infrastructures

17.1. SIEM as a Security Core

17.1.1. Hardware Configuration
17.1.2. Software Configuration
17.1.3. Network and Security/Safety Configuration

17.2. Cloud Service Configuration

17.2.1. Assigning Permissions to my Cloud Server
17.2.2. Security Configuration
17.2.3. Deployment of a Cloud Service

17.3. Administration of a Cloud Server

17.3.1. Management of Storage Units
17.3.2. Network Management
17.3.3. Security Copies Management

7.4. Persistence

17.4.1. Decoupling our Cloud Service
17.4.2. Configuration of Persistence Service
17.4.3. BB.DD Integration with our CloudService

17.5. Auto Scaling

17.5.1. Image Generation of our Server
17.5.2. Creation of Marketing Groups
17.5.3. Definition of Automatic Scaling Rules

17.6. Balancing Services

17.6.1. Emergency Services
17.6.2. Generation of a Load Balancer
17.6.3. Connection of the Balancer to our Cloud Service

17.7. Content Delivery Services

17.7.1. Content Delivery Services
17.7.2. Content Delivery Service Configuration
17.7.3. CDN Integration with our Cloud Service

17.8. Configuration Parameters and Secrets

17.8.1. Configuration Parameter Management Services
17.8.2. Secrets Management Services
17.8.3. Integrating Configuration and Secrecy Services with our Cloud Service

17.9. Queues Management Services

17.9.1.  Decoupling our Application
17.9.2. Settings of Emergency Services
17.9.3. Integrating the Queue with our Cloud Service

17.10. Notification Queuing

17.10.1. Notification Services in the Cloud
17.10.2. Settings of Notification Services
17.10.3. Notifications Added to our Cloud Service

Module 18. Virtual Desktop Infrastructure (VDI)

18.1. Virtual Desktop Infrastructure (VDI)

18.1.1. The VDI. Operation
18.1.2. Advantages and Disadvantages of VDI
18.1.3. Common VDI Usage Scenarios

18.2. Hybrid and Cloud VDI Architectures

18.2.1. Hybrid VDI Architectures
18.2.2. Cloud-Based Implementation
18.2.3. Clouds VDI Management

18.3. Design and Planning of a VDI Implementation

18.3.1. Hardware and Software Selection
18.3.2. Network and Storage Infrastructure Design
18.3.3. Implementation Planning and Scaling

18.4. VDI Management

18.4.1. VDI Set-up and Configuration
18.4.2. Desktop Image and Application Management
18.4.3. Safety and compliance management
18.4.4. Availability and Performance Management

18.5. Integration of Applications and Peripherals in the VDI

18.5.1. Enterprise Application Integration
18.5.2. Integration of Peripherals and Devices
18.5.3. VDI Integration with Video Conferencing and Instant Messaging Solutions
18.5.4. VDI Integration with Online Collaboration Platforms

18.6. Optimization and Improvement of VDI

18.6.1. Optimization of Quality Service and Performance
18.6.2. Improved and scalability Efficiency
18.6.3. Improve User Experience Final Assessment

18.7. VDI Life Cycle Management

18.7.1. Hardware and Software Life Cycle Management
18.7.2. Infrastructure Migration and Replacement Management
18.7.3. Support and Maintenance Management

18.8. Cloud Security Infrastructure and User Data Protection

18.8.1. Security in the the VDI Networks
18.8.2. Protection of Data Stored in the VDI
18.8.3. User Safety    Protection of Privacy on the Internet

18.9. Advanced VDI Use Cases

18.9.1. Using VDI for Secure Remote Access
18.9.2. Using VDI for Virtualization of Specialized Applications
18.9.3. Using VDI for Mobile Device Management

18.10. Trends and Future of VDI

18.10.1. New Technologies and trends in VDI Fields
18.10.2. Predictions on the Future of VDI
18.10.3. Future Challenges and Opportunities for DV

Module 19. Infrastructure Operation-as-Code (IAC)

19.1. Infrastructure as-Code, IAC

19.1.1. IaC, Infrastructure as Code
19.1.2. Infrastructure Management. Evolution
19.1.3. Advantages of the IaC

19.2. Strategies for TSI Definition

19.2.1. Requirements Analysis
19.2.2. Imperative Definition
19.2.3. Definition of Statement

19.3. IAC Tools

19.3.1. Objectives of the Educational and Vocational Guidance Plan
19.3.2. Proprietary Tools
19.3.3. Third-party Tools

19.4. Evolution of Infrastructure as a Codes

19.4.1. IaC on Kubernetes
19.4.2. Platform as Code
19.4.3. Compliance as Code

19.5. IAC in Devops

19.5.1. Flexible Infrastructures
19.5.2. Continuous Integration
19.5.3. Pipeline as code

19.6. IAC - VPC - Proprietary Tools 

19.6.1. Design of a VPC
19.6.2. Solution Uniqueness
19.6.3. Validation and Analysis 

19.7. IAC - Serverless - Proprietary Tools

19.7.1. Design of a Serverless Solution
19.7.2. Solution Uniqueness
19.7.3. Validation and Analysis 

19.8. IAC - VPC - Third Party Tools

19.8.1. Design of a VPC
19.8.2. Solution Uniqueness
19.8.3. Validation and Analysis 

19.9. IAC -  Serverless - Third Party Tools

19.9.1. Design of a Serverless Solution
19.9.2. Solution Uniqueness
19.9.3. Validation and Analysis 

19.10. IAC Comparison Future Trends

19.10.1. Assessment of Proprietary Solutions
19.10.2. Assessment of Third-Party Solutions
19.10.3. Future lines

Module 20. Monitoring and Backup of Cloud Infrastructures

20.1. Monitoring and Backup of Cloud Infrastructures

20.1.1. Benefits of Backup in Clouds
20.1.2. Backup Types
20.1.3. Benefits of Monitoring in Clouds
20.1.4. Types of Monitoring

20.2. Availability and Security of systems in Cloud Infrastructures

20.2.1. Main Factors
20.2.2. Most Demanded Uses and Services
20.2.3. Evolution

20.3. Types of backup services in Cloud Infrastructures

20.3.1. Total Backup 
20.3.2. Backup increase
20.3.3. Differential Backup
20.3.4. Other Types of Backup

20.4. Strategy, Planning and Management of  Cloud Infrastructure Backups

20.4.1. Establishment of Objectives and Scope
20.4.2. Backup copy type
20.4.3. Good Practices

20.5. Cloud Infrastructure Continuity Flat

20.5.1. Strategy Continuity Plan
20.5.2. Types of Plans
20.5.3. Creating a Continuity Plan

20.6. Monitoring Types in Cloud Infrastructures

20.6.1. Performance Monitoring
20.6.2. Availability Monitoring 
20.6.3. Event Monitoring
20.6.4. Log Monitoring
20.6.5. Network Traffic Monitoring

20.7. Strategy, Tools and Techniques for Monitoring  Cloud Infrastructures

20.7.1. How to set objectives and scope
20.7.2. Types of Monitoring
20.7.3. Good Practices

20.8. Continuous Improvement in Cloud Infrastructures

20.8.1. Continuous Improvement in Operations
20.8.2. Key performance metrics (KPIs) in the cloud
20.8.3. Designing a continuous improvement plan in the cloud

20.9. Studies Cases in Cloud Infrastructures

20.9.1. Study Case Backup
20.9.2. Study Case Monitoring
20.9.3. Difficulties and Good Practices

20.10. Cloud Infrastructure Case Studies

20.10.1. Laboratory 1
20.10.2. Laboratory 2
20.10.3. Laboratory 3

A program that offers you a practical approach through the numerous case studies in Cloud Infrastructures”