Thanks to this Master's Degree, you will be able to occupy relevant positions in the competitive Cloud Computing sector"

Software development and Cloud environments have led to the emergence of a new professional figure in the field of new technologies. Companies, in a short time, have understood the advantages of using Cloud Computing technology. In this scenario, the IT professionals have an opportunity to advance in an emerging field.

This Master's Degree from TECH brings together a teaching team competent in the field of Cloud technologies and with extensive experience in the sector. Their knowledge provides students with all the necessary tools to learn about the different cloud providers, therefore mastering all the technologies offered by the major distributors of Cloud solutions. Likewise, The IT professional, guided by experts in the field, will delve into the most relevant concepts and tools currently relevant in data persistence such as Data Lakes.

This 12-month program delves into the virtualization and containerization of applications that have made the systems administration sector evolve and are essential today. All this, from a theoretical-practical perspective designed for future Cloud architects, DevOps or Cloud infrastructure specialists.

An excellent opportunity for professionals who wish to improve their professional aspirations through this 100% online format. They only need an electronic device with internet connection to access the library of multimedia resources and practical simulation cases, which will facilitate learning and give them the flexibility to combine it with their most demanding professional and personal responsibilities..

Become an expert in Cloud Programming, thanks to this Master's Degree. Growing in a highly competitive industry"

This Master's Degree in Cloud Programming contains the most complete and up-to-date program on the market. The most important features include: 

• Practical cases presented by experts in Cloud Programming
• The graphic, schematic and practical contents of the book provide technical and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies 
• 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

Microsoft Azure, Amazon Web Services and Google Cloud are the main Cloud platforms for companies. Master all its possibilities with this Master's Degree. Enroll now”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

Its multimedia content, developed with the latest educational technology, will allow professionals to learn in a contextual and situated learning environment, i.e., a simulated environment that will provide immersive education programmed to prepare in real situations.

The design of this program focuses on Problem-Based Learning, by means of which professionals must try to solve the different professional practice situations that arise during the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts. 

Deepen your knowledge and become a specialist in Cloud infrastructure, mastering the most modern Cloud Native technologies and architectures"

Learn at your own pace, without fixed schedules and from anywhere with the online methodology offered by TECH in all its programs"

The syllabus has been designed based on the exhaustive requirements of the teaching team that makes up this program. In this way, a syllabus has been established consisting of ten modules that offer a broad and detailed vision of Cloud environments, the different existing tools and their possibilities in an emerging field. The IT professional who studies this program will be able to program with Cloud Natives applications, design and implement a secure network or perform real-time Cloud Programming. All of this is supported by extensive multimedia content rich in detailed videos, additional readings and real practical examples that complement this teaching.

TECH offers you a curriculum with quality content and a current and innovative approach to Cloud environments"

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. Network 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. Private Cloud

1.7.1. Private Cloud
1.7.2. Architecture and Costs
1.7.3. Private 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 from 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 Mechanisms

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 Mode 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. Orchestration 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 Fourth 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. Streaming Processing 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. Programming 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. Machine Learning for Streaming Data Processing

8.8.1. Machine 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 Mechanisms
9.2.3. Service Architectures

9.3. Service Oriented Architectures

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

9.4. SOAP Service Oriented Architecture

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 Architecture Approach. Micro-Service Use
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 Service 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.  Agile Use

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

Be a true professional. Reduce risks in the Cloud and guarantee security to the companies you work for"