Prof. Paolo Garza

• Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available.

Argomenti del programma: Lectures and classroom exercises (62 hours) • Introduction to Big data: characteristics, problems, opportunities (3 hours) • Design of a big data pipeline (3 hours) • Hadoop and its ecosystem: infrastructure and basic components (4.5 hours) • Map Reduce programming paradigm (9 hours) • Spark Architecture and RDD-based programming paradigm (21.5 hours) • Data streaming analytics (Kafka, Spark Streaming, Storm) (12…

• Copies of the slides used during the lectures, examples of written exams and exercises, and manuals for the activities in the laboratory will be made available. All teaching material is downloadable from the course website or the Portal.

Argomenti del programma: Lectures (45 hours) • Introduction to Big data: characteristics, problems, opportunities (3 hours) • Hadoop and its ecosystem: infrastructure and basic components (1.5 hours) • Map Reduce programming paradigm (10.5 hours) • Spark: Spark Architecture, RDD-based and Spark SQL-based programming (15 hours) • Streaming data analytics: Spark Streaming (6 hours) • Data mining and Machine learning library: Spark MLlib (7.5…