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Distributed Systems

The Distributed Systems course offers an in-depth exploration of systems that operate across various nodes, utilizing network communication and coordination.

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Certificate :

After Completion

Start Date :

10-Jan-2025

Duration :

30 Days

Course fee :

$150

COURSE DESCRIPTION:

  1. The Distributed Systems course offers an in-depth exploration of systems that operate across various nodes, utilizing network communication and coordination.

  2. It addresses the foundational principles, design methodologies, and implementation strategies of distributed systems, along with their practical applications in areas like cloud computing, distributed databases, and extensive frameworks such as Hadoop and blockchain.

  3. Participants will delve into essential topics including system architecture, consensus mechanisms, fault tolerance, data replication, and scalability.

  4. The course prioritizes practical experience through projects and case studies, enabling students to apply theoretical insights to real-world challenges in distributed settings.

  5. This course is well-suited for software engineers, system architects, and computer science enthusiasts aiming to advance their careers in large-scale system design, cloud technologies, or distributed computing.

CERTIFICATION:

  1. Upon finishing the program, participants will receive a Certificate in Distributed Systems, which confirms their proficiency in several areas.

  2. This includes the design and implementation of distributed systems and algorithms, a grasp of critical challenges such as consistency, fault tolerance, and scalability, as well as the application of distributed computing principles to practical systems.

  3. To qualify for certification, participants must complete all course modules and assignments, present a capstone project, and attain a minimum score of 70% on quizzes and the final exam.

  4. This certification signifies preparedness for positions in distributed systems engineering, cloud architecture, and the development of large-scale applications.

LEARNING OUTCOMES:

By the conclusion of the course, participants will possess the skills to:

  1.  participants will acquire a comprehensive understanding of the core principles and architecture underlying distributed systems.

  2. They will be equipped to analyze and tackle challenges inherent in distributed environments, including issues related to concurrency, consistency, and fault tolerance.

  3. Participants will learn to design distributed algorithms that facilitate coordination, consensus, and replication. They will also gain the skills necessary to implement scalable and resilient distributed systems utilizing frameworks such as Hadoop and Spark.

  4. Additionally, they will develop distributed applications leveraging tools and platforms like Kafka, ZooKeeper, and gRPC, while applying knowledge of distributed databases and storage systems, including the CAP theorem and eventual consistency.

  5. The course will also cover practical applications of distributed systems in areas like blockchain, cloud computing, and IoT, culminating in a capstone project that emphasizes the design and deployment of a distributed system.

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Course Curriculum

Introduction to Distributed Systems
  1. What are distributed systems?
  2. Key characteristics: Scalability, fault tolerance, and concurrency
  3. Applications of distributed systems in real-world scenarios
Architectural Models
  1. Client-server model
  2. Peer-to-peer model
  3. Microservices architecture and cloud-based distributed systems
Communication in Distributed Systems
  1. Inter-process communication (IPC)
  2. Remote Procedure Calls (RPC) and RESTful APIs
  3. Message passing and middleware
Synchronization in Distributed Systems
  1. Concept of time in distributed systems: Logical clocks and vector clocks
  2. Distributed mutual exclusion and leader election algorithms
  3. Challenges of synchronization in a distributed environment
Distributed Storage and Databases
  1. Distributed file systems: HDFS and Amazon S3
  2. Distributed databases: CAP theorem, consistency, availability, and partition tolerance
  3. Examples: Apache Cassandra, MongoDB, and Google Bigtable
Fault Tolerance and Reliability
  1. Fault detection and recovery techniques
  2. Replication strategies for reliability
  3. Consensus algorithms: Paxos, Raft, and Byzantine Fault Tolerance
Security in Distributed Systems
  1. Authentication and authorization in distributed environments
  2. Encryption and secure communication protocols
  3. Handling distributed denial-of-service (DDoS) attacks
Case Studies and Applications
  1. Distributed systems in cloud computing: AWS, Azure, and Google Cloud
  2. Blockchain as a distributed system
  3. Real-world examples: Netflix, Google Search, and distributed gaming

Training Features

Practical Hands-On Learning

Work on real-world problems to understand how distributed systems operate.

Algorithm Implementation

Learn to implement critical distributed algorithms such as Paxos and Raft.

Case Study Driven Approach

Analyze successful distributed systems like Hadoop, Kafka, and Kubernetes.

Mentorship and Community Support

Access to experts in distributed systems for guidance and group learning.

Interactive Simulation

Visualize concepts such as consistency models and fault tolerance using tools and simulators.

Certification

Earn a certificate that validates your expertise in distributed systems.

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