Postgraduate diploma Distributed Computing

Without the existence of so-called distributed computing systems, it would be impossible to understand technologies such as big data or an infinite number of existing Internet services. Advances in inter-process communication, cryptographic security, and distributed transactions have enabled unprecedented data replication, establishing an undeniable evolutionary framework for all computer scientists. During this diploma, you will extensively analyze various programming models focused on Distributed Computing, offering a distinct perspective that encompasses both theoretical foundations and a wide array of practical applications. The teaching team has made significant efforts to design a comprehensive and thorough program that will greatly propel the career path of computer scientists.

University certificate

duration

6 months

Modality

Online

Schedule

At your own pace

Exams

Online

start date

Credits

18 ECTS

financing up to

6 months

Price(first year)

See price

The world's largest faculty of information technology”

Introduction to the Program

Take a step forward with this Postgraduate diploma in Distributed Computing and delve into the most cutting-edge techniques in computer science”

When discussing the widespread use of smartphones in daily life or the introduction of 5G as the new communication standard, a new realm of possibilities is emerging for computer scientists proficient in Distributed Computing. As time progresses, the degrees of processing and processing speed will continue to increase, which emphasizes the need for computer professionals to be adequately prepared to program at a higher level.

Indeed, this Postgraduate diploma is designed to provide computer professionals with the necessary knowledge and skills in the field of Distributed Computing. Students will receive a comprehensive education in everything related to Distributed Computing, tailored to the demands of today's market, thanks to a teaching team with extensive experience in managing and directing IT projects of this type.

In addition, the degree is offered as a fully online program, making it convenient to integrate with other personal or professional commitments. There are no in-person classes or set schedules; instead, computer scientists have the freedom to download the entire syllabus and allocate their study hours according to their own preferences.

Achieve a significant promotion in your professional career by showcasing your advanced programming and distributed management skills through postgraduate diploma”

This Postgraduate diploma in Distributed Computing contains the most complete and up-to-date program on the market. The most important features include:

The program includes the development of case studies presented by experts in Parallel and Distributed Computing
The program is designed with graphical, schematic, and highly practical content, which gathers essential information about disciplines that are crucial for the professional practice
Practical exercises where self-assessment can be used to improve learning
Its special emphasis on innovative methodologies
The program incorporates theoretical lessons, interactive question-and-answer sessions with experts, and individual reflection assignments
Content that is accessible from any fixed or portable device with an Internet connection

Gain valuable tips and insights from highly successful professionals who have been leaders in international development projects”

The teaching staff of the program consists of professionals from the industry who bring their valuable work experience to the table. Additionally, renowned specialists from leading societies and prestigious universities contribute their expertise to enrich the program.

The program's multimedia content, created using state-of-the-art educational technology, enables professionals to learn in a contextual and situated learning environment. This simulated environment offers immersive education specifically designed to prepare individuals for real-world situations.

The program's design emphasizes Problem-Based Learning, requiring professionals to actively solve various real-world practice situations that are presented to them throughout the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts.

The program's design emphasizes Problem-Based Learning, requiring professionals to actively solve various real-world practice situations that are presented to them throughout the academic year. For this purpose, the student will be assisted by an innovative interactive video system created by renowned and experienced experts"

TECH will provide you with the support and resources you need to achieve your most ambitious professional goals"

Syllabus

The structure and content of this Postgraduate diploma have been meticulously designed to provide students with maximum comfort and efficiency. The program consists of three modules, each of which is further divided into 10 different topics. These topics cover a wide range of areas, including the characteristics and design of Distributed Systems, Distributed Computing oriented programming, and the applications of Distributed Computing in the current landscape.

You will have access to a wide range of multimedia resources which include summary videos, detailed instructional videos, and motivational videos created by the teachers”

Module 1. Distributed Systems in Computing

1.1. Distributed Systems

1.1.1. Distributed Systems (SD)
1.1.2. Proof of the CAP Theorem (or Brewer's Conjecture)
1.1.3. Fallacies of Distributed Systems Programming
1.1.4. Ubiquitous Computing

1.2. Distributed Systems Features

1.2.1. Heterogeneity
1.2.2. Extensibility
1.2.3. Security/Safety
1.2.4. Scales
1.2.5. Fault Tolerance
1.2.6. Concurrency
1.2.7. Transparency

1.3. Networks and Interconnection of Distributed Networks

1.3.1. Networks and Distributed Systems.
1.3.2. Networks Available to Create a Distributed System. Typology
1.3.3. Network Protocols Distributed vs. Centralized
1.3.4. Interconnection of Networks. Internet

1.4. Communication Between Distributed Processes

1.4.1. Communication between nodes of a D.S. Problems and failures
1.4.2. Mechanisms to Implement Over RPC and RDMA to Avoid Failures
1.4.3. Mechanisms to Implement in the Software to Avoid Failures.

1.5. Distributed Systems Design

1.5.1. Efficient Design of Distributed Systems (SD)
1.5.2. Patterns for Distributed Systems (SD) Programming
1.5.3. Service Oriented Architecture SOA
1.5.4. Service Orchestration and Microservices Data Management

1.6. Distributed Systems Operation

1.6.1. Systems Monitoring
1.6.2. Implementing an Efficient Logging System in a DS
1.6.3. Monitoring in Distributed Networks
1.6.4. Use of a Monitoring Tool for an SD Prometheus and Grafana Prometheus y Grafana

1.7. System Replication

1.7.1. System Replication Typology
1.7.2. Immutable Architecture
1.7.3. Container Systems and Virtualizing Systems as Distributed Systems
1.7.4. Blockchain Networks as Distributed Systems

1.8. Distributed Multimedia Systems

1.8.1. Distributed Exchange of Images and Videos. Problems
1.8.2. Multimedia Object Servers
1.8.3. Network Topology for a Multimedia System
1.8.4. Analysis of Distributed Multimedia Systems: Netflix, Amazon, Spotify, etc.
1.8.5. Distributed Multimedia Systems in Education

1.9. Distributed File Systems

1.9.1. Distributed File Sharing. Problems
1.9.2. Applicability of the CAP Theory to Databases
1.9.3. Distributed Web File Systems: Akamai
1.9.4. IPFS Distributed Document File Systems
1.9.5. Distributed Database Systems

1.10. Security Approaches in Distributed Systems

1.10.1. Security in Distributed Systems
1.10.2. Known Attacks on Distributed Systems
1.10.3. Tools for Testing the Security of a DS

Module 2. Models and Formal Semantics. Programming oriented to distributed computing

2.1. Semantics Data Model

2.1.1. Semantics Data Model
2.1.2. Semantics Data Model. Purposes
2.1.3. Semantics Data Model. Applications

2.2. Semantic Model of Programming Languages

2.2.1. Language Processing
2.2.2. Translation and Interpretation
2.2.3. Hybrid Languages

2.3. Models of Computation

2.3.1. Monolithic Computing
2.3.2. Parallel Computing
2.3.3. Distributed Computing
2.3.4. Cooperative Computing (P2P)

2.4. Parallel Computing

2.4.1. Parallel Architecture
2.4.2. Hardware
2.4.3. Software

2.5. Distribution Models Grid Computing

2.5.1. Grid Computing Architecture
2.5.2. Grid Computing Architecture. Analysis
2.5.3. Grid Computing Architecture Applications

2.6. Distribution Models Cluster Computing

2.6.1. Cluster Computing Architecture
2.6.2. Cluster Computing Architecture Analysis
2.6.3. Cluster Computing Architecture Applications

2.7. Cluster Computing Current Tools to Implement Cluster Computing. Hypervisors

2.7.1. Market Competitors
2.7.2. VMware Hypervisor
2.7.3. Hyper-V

2.8. Distribution Models Cloud Computing

2.8.1. Architecture Cloud Computing
2.8.2. Cloud Computing Architecture. Analysis
2.8.3. Cloud Computing Architecture. Applications

2.9. Distribution Models Amazon Cloud Computing

2.9.1. Amazon Cloud Computing Functional Criteria
2.9.2. Amazon Cloud Computing Licensing
2.9.3. Amazon Cloud Computing Reference Architectures

2.10. Distribution Models Microsoft Cloud Computing

2.10.1. Microsoft Cloud Computing Functional Criteria
2.10.2. Microsoft Cloud Computing Licensing
2.10.3. Microsoft Cloud Computing Reference Architectures

Module 3. Parallel and Distributed Computing Applications

3.1. Parallel and Distributed Computing in Today's Applications

3.1.1. Hardware
3.1.2. Software
3.1.3. Importance of Timing

3.2. Climate. Climate Change.

3.2.1. Climate Applications. Data Sources
3.2.2. Climate Applications. Data Volumes
3.2.3. Climate Applications. Real Time

3.3. GPU Parallel Computing

3.3.1. GPU Parallel Computing
3.3.2. AIH vs. CPU. GPU Usage
3.3.3. GPU. Examples:

3.4. Smart Grid. Computing in Power Grids

3.4.1. Smart Grid
3.4.2. Conceptual Models. Examples:
3.4.3. Smart Grid. Example

3.5. Distributed Engine. ElasticSearch

3.5.1. Distributed Engine. ElasticSearch
3.5.2. Architecture with ElasticSearch. Examples:
3.5.3. Distributed Engine. Case Uses

3.6. Big Data Framework

3.6.1. Big Data Framework
3.6.2. Architecture of Advanced Tools
3.6.3. Big Data in Distributed Computing

3.7. Memory Database

3.7.1. Memory Database
3.7.2. Redis Solution. Case Study
3.7.3. Deployment of Solutions With In-Memory Database

3.8. Blockchain.

3.8.1. Blockchain Architecture. Components
3.8.2. Collaboration Between Nodes and Consensus.
3.8.3. Blockchain Solutions. Implementations

3.9. Distributed Systems in Medicine

3.9.1. Architecture Components
3.9.2. Distributed Systems in Medicine. Operation
3.9.3. Distributed Systems in Medicine. Applications

3.10. Distributed Systems in the Aviation Sector

3.10.1. Architecture Design
3.10.2. Distributed Systems in the Aviation Sector. Component Functionalities
3.10.3. Distributed Systems in the Aviation Sector. Applications

You will be able to better contextualize the theory taught through the numerous exercises provided for each topic”

Postgraduate Diploma in Distributed Computing.

Distributed computing is a field of computer science that focuses on the use of multiple interconnected computing systems to work together as if they were a single entity. In distributed computing, the resources of interconnected computing systems, such as processing, storage, and memory, are used together to perform specific tasks.

The development of distributed computing involves the use of techniques to coordinate and manage the resources of interconnected systems. Distributed systems use communication protocols to coordinate data exchange, control access to resources, and manage load balancing between different systems.

Applications that use distributed computing.

Web search: search engines use distributed computing to index and search for information on the web.

Scientific research: scientists use distributed computing to handle large data sets and process complex simulations.

Social networking: social networking systems use distributed computing to support large numbers of users and handle large amounts of information.

To develop distributed computing applications, it is necessary to have a wide knowledge of the fundamental principles of distributed computing. This includes knowledge of communication protocols such as TCP and UDP, distributed system architectures, distributed storage systems such as Hadoop, and distributed database systems. Experience in programming languages such as Java, Python, C++ and Ruby, which are commonly used in distributed computing application development, as well as tools and technologies specific to different distributed systems, such as Apache Spark and Kafka for Apache Hadoop, is also required.

Distributed computing is a field of computer science that focuses on the use of multiple interconnected systems to work together as if they were a single entity. The development of distributed computing involves the use of techniques to coordinate and manage the resources of interconnected systems, and requires knowledge in communication protocols, distributed system architectures, programming languages and tools specific to different distributed systems.