Big Data Analytics

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Teacher:

Dr. Massimo Callisto De Donato <massimo.callistodedonato[at]gruppofilippetti.it>

Lessons schedule:

Students Office hours:

The course gives an introduction to the Big Data models and related techniques required to perform data analysis in real world examples.
The course focuses on data with “Big Data characteristics” such as data that can generated by any kind of systems with an high volume, data that grows very fast, data highly semi-structured or un-structured.
The course highlights the correlations between Big Data and related fields of IoT and Smart Cities.
The course introduces all relevant state-of-the-art concepts, methods and technologies enabling Big Data Analysis in real world business cases.

Course Contents

Acquire knowledge and competence on Big Data methodologies, techniques and technologies.
Know most common techniques of Big Data analysis and how they apply to real world examples.
Apply Big Data Analysis techniques into practical case studies.

Introduction to Big Data
- What are Big Data
- Big Data in the real world
- Needs and Challenges of Big Data
- Characterize Big Data
Big Data models: from storage to processing
- The V model
- From datawarehouse to Big Data
- Aspect of Distributing Storage
- Distributed Processing
- NoSQL
- Distributed Search
Big Data Analisys: Methodologies and Techniques
- How enabling Big Data Processing
- The Hadoop framework
- HDFS: hadoop filesystem
- Computational framework: MapReduce and YARN
- HBase as NoSQL database
- Other related hadoop processing frameworks
- Practical Examples
- Apache Spark
- Apache Cassandra
- Batch processing Vs RealTime processing
- Apache Spark Streaming
- Apache Storm
Big Data Analysis applications
- Big Data in practice: connecting with IoT
- Use cases scenario
Advance Big Data Analysis
- Distribuited graph modelling framework
- Machine Learning
- Baysian Newtork

Course Slides

Exam Dates A.Y. 2017/2018

Exam rules:

Exam Results