In this Postgraduate diploma, you will be university able to balance the efficiency of the most advanced learning methods with the flexibility of a program created to adapt to your possibilities of dedication, without losing quality"

Data is the fundamental raw material for research and knowledge advancement. In recent years, there has been an increase in initiatives that have made the creation, access, use and preservation of data as another axis within the work of communities linked to research in various areas of knowledge. This program offers specialized knowledge in data management, focusing on its typology and life cycle and practical approach through the available resources.  

Today there are a large number of applications that we use from our mobile or any other smart device that access services hosted on platforms that are being used by hundreds of thousands of users simultaneously. There are a multitude of applications supported from platforms that must not only serve "human" users but also millions of connected devices such as, IoT modules, smart speakers, etc. 

The role of system administrators has now changed from being an operator who modifies system configuration to implement a series of policies to being more of a software architect who designs and implements specific algorithms which will alter the configuration of a series of resources to meet specific requirements demanded at a given time by a specific situation. 

On the other hand, during the last decade, in software engineering, especially in the backend, the set of concepts, tools and technologies around distributed systems and data management and processing has grown considerably. In today's rapidly changing landscape, it is critical that students understand the underlying technology of many of today's systems that are highly demanding in terms of scalability, performance and reliability.  The ultimate goal of this understanding is to be in the best position to make good decisions in distributed system design, among other issues of interest. 

As it is a 100% online program, students will not have to give up personal or professional obligations. Upon completion of the program, students will have updated their knowledge and will be in possession of an incredibly prestigious qualification that will allow them to advance both personally and professionally.

Learn to analyze classical system models and identify shortcomings for use in distributed applications"

This Postgraduate diploma in High Volume and Heterogeneous Category Information Processing Architectures contains the most complete and up-to-date educational program on the market. The most important features include:

• The development of case studies presented by experts in High Volume and Heterogeneous Category Information Processing Architectures 
• 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
• 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

With the best developed distance learning systems, this Postgraduate diploma will allow you to learn, in a contextual way, the practical skills that you need"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this training program, as well as renowned specialists from leading 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 immersive education programmed to learn in real situations.   

This program is designed around Problem-Based Learning, whereby the professional 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.   

An intensive professional growth program that will allow you to intervene in a sector with a growing demand for professionals"

A comprehensive program for IT professionals, which will allow them to compete among the best in the sector"

The syllabus has been designed based on educational efficiency, carefully selecting the contents to offer a comprehensive course, which includes all the fields of study that are essential to achieve real knowledge of the subject. Including the latest updates and aspects of the field. Therefore, a curriculum has been established whose modules offer a broad perspective of High Volume and Heterogeneous Category Information Processing Architectures. From first module, students will see their knowledge expanding, which will enable them to develop professionally, knowing that they can count on the support of a team of experts.

Succeed with the best and acquire the knowledge and skills you need to embark on the High Volume and Heterogeneous Category Information Processing Architectures"

Module 1. Data Types and Data Life Cycle

1.1. Statistics

1.1.1. Statistics: Descriptive Statistics, Statistical Inferences
1.1.2. Population, Sample, Individual
1.1.3. Variables: Definition, Measurement Scales

1.2. Types of Data Statistics

1.2.1. According to Type

1.2.1.1. Quantitative: Continuous Data and Discrete Data
1.2.1.2. Qualitative: Binomial Data, Nominal Data and Ordinal Data

1.2.2. According to their Shape

1.2.2.1. Numeric
1.2.2.2. Text
1.2.2.3. Logical

1.2.3. According to its Source

1.2.3.1. Primary
1.2.3.2. Secondary

1.3. Life Cycle of Data

1.3.1. Stages of the Cycle
1.3.2. Milestones of the Cycle
1.3.3. FAIR Principles

1.4. Initial Stages of the Cycle

1.4.1. Definition of Goals
1.4.2. Determination of Resource Requirements
1.4.3. Gantt Chart
1.4.4. Data Structure

1.5. Data Collection

1.5.1. Methodology of Data Collection
1.5.2. Data Collection Tools
1.5.3. Data Collection Channels

1.6. Data Cleaning

1.6.1. Phases of Data Cleansing
1.6.2. Data Quality
1.6.3. Data Manipulation (with R)

1.7. Data Analysis, Interpretation and Evaluation of Results

1.7.1. Statistical Measures
1.7.2. Relationship Indices
1.7.3. Data Mining

1.8. Data Warehouse

1.8.1. Elements of a Data Warehouse
1.8.2. Design
1.8.3. Aspects to Consider

1.9. Data Availability

1.9.1. Access
1.9.2. Uses
1.9.3. Security

Module 2. Scalable and Reliable Mass Data Usage Systems

2.1. Scalability, Reliability and Maintainability

2.1.1. Scales
2.1.2. Reliability
2.1.3. Maintainability

2.2. Data Models

2.2.1. Evolution of Data Models
2.2.2. Comparison of Relational Model with Document-Based NoSQL Model
2.2.3. Network Model

2.3. Data Storage and Retrieval Engines

2.3.1. Structured Log Storage
2.3.2. Storage in Segment Tables
2.3.3. Trees B

2.4. Services, Message Passing and Data Encoding Formats

2.4.1. Data Flow in REST Services
2.4.2. Data Flow in Message Passing
2.4.3. Message Sending Formats

2.5. Replication

2.5.1. CAP Theorem
2.5.2. Consistency Models
2.5.3. Models of Replication Based on Leader and Follower Concepts

2.6. Distributed Transactions

2.6.1. Atomic Operations
2.6.2. Distributed Transactions from Different Approaches Calvin, Spanner
2.6.3. Serialisability

2.7. Partitions

2.7.1. Types of Partitions
2.7.2. Indexes in Partitions
2.7.3. Partition Rebalancing

2.8. Batch Processing

2.8.1. Batch Processing
2.8.2. MapReduce
2.8.3. Post-MapReduce Approaches

2.9. Data Stream Processing

2.9.1. Messaging Systems
2.9.2. Persistence of Data Flows
2.9.3. Uses and Operations with Data Flows

2.10. Use Cases. Twitter, Facebook, Uber

2.10.1. Twitter: The Use of Caches
2.10.2. Facebook: Non-Relational Models
2.10.3. Uber: Different Models for Different Purposes

Module 3. System Administration for Distributed Deployments 

3.1. Classic Administration: The Monolithic Model

3.1.1. Classical Applications: The Monolithic Model
3.1.2. System Requirements for Monolithic Applications
3.1.3. The Administration of Monolithic Systems
3.1.4. Automation

3.2. Distributed Applications: The Microservice

3.2.1. Distributed Computing Paradigm
3.2.2. Microservice-Based Models
3.2.3. System Requirements for Distributed Models
3.2.4. Monolithic Applications vs. Distributed Applications

3.3. Tools for Resource Exploitation

3.3.1. “Iron” Management
3.3.2. Virtualization
3.3.3. Emulation
3.3.4. Paravirtualization

3.4. IaaS, PaaS and SaaS Models

3.4.1. LaaS Model
3.4.2. PaaS Model
3.4.3. SaaS Model
3.4.4. Design Patterns

3.5. Containerisation

3.5.1. Virtualization with Cgroups
3.5.2. Containers
3.5.3. From Application to Container
3.5.4. Container Orchestration

3.6. Clustering

3.6.1. High Performance and High Availability
3.6.2. High Availability Models
3.6.3. Cluster as SaaS Platform
3.6.4. Cluster Securitization

3.7. Cloud Computing

3.7.1. Clusters vs Clouds
3.7.2. Types of Clouds
3.7.3. Cloud Service Models
3.7.4. Oversubscription

3.8. Monitoring and Testing

3.8.1. Types of Monitoring
3.8.2. Visualization
3.8.3. Infrastructure Tests
3.8.4. Chaos Engineering

3.9. Case Study: Kubernetes

3.9.1. Structure
3.9.2. Administration
3.9.3. Deployment of Services
3.9.4. Development of Services for K8S

3.10. Case Study: OpenStack

3.10.1. Structure
3.10.2. Administration
3.10.3. Deployment
3.10.4. Development of Services for OpenStack

All the subjects and areas of knowledge have been compiled in a complete and up-to-date syllabus, in order to bring students to the highest theoretical and practical level"