The course aims at providing: • Knowledge of the main problems and opportunities arising in the big data context and technological characteristics of the infrastructures and distributed systems used to deal with big data (e.g., Hadoop and Spark). • Ability to write distributed programs to process and analyze big data by means of novel programming paradigms: the Map Reduce and Spark programming paradigms • Ability to write distributed programs to process and analyze streaming data • Knowledge of state of the art machine learning library for big data (e.g., MLlib) • Ability to design a big data pipeline

• Python language • Basic Object-oriented programming skills • Basic knowledge of the SQL language and the relational data model • Knowledge of standard centralized machine learning algorithms

Lectures and classroom exercises (62 hours) • Introduction to Big data: characteristics, problems, opportunities (3 hours) • Big data architectures (3 hours) • Hadoop and its ecosystem: infrastructure and basic components (3 hours) • Map Reduce programming paradigm (9 hours) • Spark Architecture and RDD-based programming paradigm (14 hours) • Spark SQL and DataFrames (12 hours) • Data mining and Machine learning libraries: MLlib (4.5 hours) • Graph analytics: Spark GraphX and GraphFrame (4.5 hours) • Data streaming analytics (Kafka, Spark Streaming, Storm) (9 hours) Laboratory activities (18 hours) • Development of big data applications by means of Hadoop and Spark (18 hours)

The course consists of Lectures and classroom exercises (62 hours) and Laboratory sessions (18 hours). The laboratory sessions are focused on the main topics of the course (MapReduce, Spark, and MLlib). The Laboratory sessions allow experimental activities on the most widespread open-source and state of the art big data frameworks.

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 and the teaching portal. Reference books: • Matei Zaharia, Bill Chambers. Spark: The Definitive Guide (Big Data Processing Made Simple). O'Reilly Media, 2018. • Advanced Analytics and Real-Time Data Processing in Apache Spark. Packt Publishing, 2018. • Tom White. Hadoop, The Definitive Guide. (Third edition). O'Reilly Media, 2015. • Matei Zaharia, Holden Karau, Andy Konwinski, Patrick Wendell. Learning Spark (Lightning-Fast Big Data Analytics). O’Reilly, 2015.

Lecture slides; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions; Video lectures (current year); Video lectures (previous years);

You can take this exam before attending the course

Exam: Computer-based written test in class using POLITO platform;

The exam aims at assessing (i) the ability of the students to write distributed programs to process and analyze big data using novel programming paradigms (MapReduce and Spark-RDD-based programming paradigms) and (ii) the knowledge of the students of the main issues related to the big data topic and the technological infrastructures and distributed systems that are used to deal with big data. The exam consists of a written onsite PC-based exam (with the Exam platform) that lasts 1.5 hours. Specifically, the written online test is composed of two parts: - 2 programming exercises (structured as open questions) based on MapReduce- and Spark-based programming (27 points) - 2 multiple-choice questions on all the topics addressed during the course (4 points). The programming exercises aim at evaluating the ability of the students to write distributed programs to analyze big data using the programming paradigms introduced in the course. The multiple-choice questions are used to evaluate the knowledge of the theoretical concepts of the course and, in particular, the knowledge of the main technological infrastructures and distributed systems (Hadoop and Spark) used to deal with big data. The evaluation of the programming exercises is based on the correctness and efficiency of the proposed solutions. For each multiple-choice question, the students achieve two points if the answer is correct and zero points if the answer is wrong or missing. The exam is open book. -Any electronic devices (PC, laptop, mobile phone, calculators, etc.) apart from the PC used to take the test are not allowed. The exam is passed if the mark of the written exam is greater than or equal to 18 points. If the mark of the written exam is greater than 30, then the final mark will be "30 e lode".

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.