2020 Cloud Computing and Big Data Lecture 5 Map-Reduce Computing Paradigm Part2 ?

2020 Cloud Computing and Big Data Lecture 5 Map-Reduce Computing Paradigm - Part Two ?

Parallel & Scalable Machine Learning & Deep Learning
16 university lectures with additional practical lectures for hands-on exercises in context
University of Iceland, School of Engineering and Natural Sciences
Faculty of Industrial Engineering, Mechanical Engineering and Computer Science

Fall 2020

Course Outline

1. Cloud Computing & Big Data Introduction
2. Machine Learning Models in Clouds
3. Apache Spark for Cloud Applications
4. Virtualization & Data Center Design
5. Map-Reduce Computing Paradigm
6. Deep Learning driven by Big Data
7. Deep Learning Applications in Clouds
8. Infrastructure-As-A-Service (IAAS)
9. Platform-As-A-Service (PAAS)
10. Software-As-A-Service (SAAS)
11. Big Data Analytics & Cloud Data Mining
12. Docker & Container Management
13. OpenStack Cloud Operating System
14. Online Social Networking & Graph Databases
15. Big Data Streaming Tools & Applications
16. Epilogue

Lecture Outline

Part One: Map Reduce Approach
Big Data Analytics Requirements & Map-Reduce via Apache Hadoop
Motivation for Hiding Computing Complexity & Increase Reliability
Divide and Conquer Paradigm & Three ‘phases’ of Map-Reduce
Understanding Key-Value Data Structures for Map-Reduce
Open Source Apache Hadoop & HBase in Facebook Applications

Part Two: Map-Reduce Ecosystem in Clouds & Applications
Role of Reliability using Statistics & Hadoop Distributed File System
Replication Concept & Benefits for Cloud ‘Big Data’ Applications
Amazon Web Services offering Elastic MapReduce & Ecosystem Tools
Using Containers with Docker & Cloud Deployment using Kubernetes
OpenStack Cloud Operating System & Map-Reduce Limits