Cloud Computing for Big data Analysis

We will discuss the characteristics and benefits of Clouds, introduce the MapReduce framework based on Hadoop, discuss how to design efficient MapReduce algorithms and present the state-of-the-art in MapReduce algorithms. Furthermore, we will cover the basics and some advanced methods of data analysis in the cloud. In particular, we will present the three fundamental analysis techniques: association rules, clustering, and supervised learning, with a cloud-computing related twist. The main part of the course are the following: 1. Introduction: Distributed computing for Big Data problems, Data Management. 2. Concepts and techniques for Cloud computing: Cloud characteristics, Cloud benefits (elasticity, pay-as-you-go, scale economies), Cloud service models (IaaS, PaaS, SaaS). 3. Programming solutions for Big Data problems: MapReduce programming model and patterns (Hadoop programming), PIG, Streaming Data Analysis in the Cloud. 4. Big Data Analysis Technique: Basic Techniques for Mining Big Data (Association Rules, Clustering, Supervised Learning). Prerequisites A basic knowledge of Computer Architectures, Algorithm design, object oriented programming, Principles of Concurrent and Distributed Programming