Big Data: Architectures and Data Analytics (2021/2022)

Big Data: Architectures and Data Analytics (2021/2022)

General Information

SSD: ING-INF/05

CFU: 6

Professor: Paolo Garza

Teaching Assistant: Luca Colomba

Announcements

  • 16-09-21: The first lecture is scheduled for September 27, 2021 at 16:00 in Classroom R1
  • 23-09-21: No lab activities during the first two weeks of the course
  • 27-09-21: I created a page for “01QYDOV – Big data: architectures and data analytics” on Piazza: piazza.com/polito.it/fall2021/01qydov
    Piazza is a Q&A system that can be used to manage questions and answers offline. You can use it, instead of the email, when you have questions that are of interest also for other students (e.g., questions on the proposed solutions, problems with the configuration of the used software, etc.). There are different “channels” (lectures, lab, others) related to different topics. You can publish both public or private questions.
    I will answer periodically to your questions (I will try to answer them daily).
  • 10-10-21: First lab activity this week
    • Monday, October 11, 17:30 – 19:00 – LAIB1 – TEAM 1
    • Wednesday, October 13, 14:30 – 16:00 – LAIB1 – TEAM 2
  • 10-10-21: The lecture scheduled for Tuesday, October 12 at 10:00 will start at 11:30 and will last only 1.5 hours (from 11:30 to 13:00)

Teaching Material

  • Introduction to the course content and exam rules (slides)
  • Introduction to Big Data (slides)
  • Big Data Architectures (slides)
  • Hadoop and MapReduce
    • Introduction to Apache Hadoop and the MapReduce programming paradigm (slides)
      • Interaction with HDFS and Hadoop by means of the command line (slides)
    • Hadoop implementation of MapReduce (slides)
      • BigData@Polito environment + Jupyter – How to submit MapReduce jobs on BigData@Polito (slides)
    • MapReduce and Hadoop – Advanced Topics: Multiple inputs, Multiple outputs, Distributed cache (slides)
    • MapReduce – Design patterns – Part 1 (slides) (slides without black background) (slides)
    • MapReduce – Design patterns – Part 2 (slides)
    • MapReduce – Relational Algebra/SQL operators (slides)
  • Spark
    • Introduction to Apache Spark (slides)
      • How to submit Spark applications (slides)
    • RDD-based programs RDDs
      • Creation, basic transformations and actions (slides)
      • Key-value pair RDDs: transformations and actions on PairRDDs (slides)
      • DoubleRDDs (slides)
      • Advanced Topics: Cache, accumulators, broadcast variables (slides) – Updated on November 6. 2021
    • Spark SQL, Datasets and DataFrames (slides)
    • Data Mining
      • Recap data mining tasks (slides) – From the “Data Science And Database Technology” course
    • Spark MLlib
      • Introduction and Classification of structured data (slides)
        • Logistic Regression example code (zip)
        • Decision Trees example code (zip)
        • Decision Trees and Categorical class label example code (zip)
      • Classification of textual data (slides)
        • Textual data classification example code (zip)
      • Classification and Parameter tuning (slides)
        • Parameter tuning example code (zip)
      • Clustering of structured data (slides)
        • Clustering example code (zip)
      • Itemset and Association rule mining (slides)
        • Itemset and Association rule mining example code (zip)
      • Linear regression (slides)
        • Linear regression example code (zip)
    • Spark Streaming (slides)
      • Examples: Word Count – Streaming versions (zip)

Exercise

If you use your PC to write and run your code, import the projects based on Maven (those projects can be run locally).
If you use the PC available in the LAB, import the Eclipse projects with libraries (those projects cannot be run locally but only on the cluster exporting the jar file of the project).


Laboratory Material

No lab activities during the first two weeks of the course

Team 1: Students from A to L – Monday from 17:30 to 19:00 – LAIB1

Team 2: Students from M to Z – Wednesday from 14:30 to 16:00 – LAIB1

  • Lab1: Hadoop and MapReduce
    • Problem specification (pdf)
    • Basic project and small example data set (Lab1_BigData_with_libraries.zip)
    • Basic project based on Maven – Use this version of the project to run the MapReduce application locally on your own PC (DO NOT USE IT AT LAIB1)
      • Import it using Import/Maven/Existing Maven Projects
      • Bigger data set: finefoods_text.txt (zip)
  • Lab4: Normalized ratings for product recommendations with Hadoop MapReduce
  • Lab5: Filter data and compute basic statistics with Apache Spark
  • Lab6: Frequently bought/reviewed together application with Apache Spark
  • Lab7: Bike sharing data analysis
    • Expected output
      • Execution on sample data (sampleData/registerSample.csv and sampleData/stations.csv) and minimum criticality threshold = 0.4 (part-00000)
      • Execution on complete data (/data/students/bigdata-01QYD/Lab7/register.csv and /data/students/bigdata-01QYD/Lab7/stations.csv) and minimum criticality threshold = 0.6 (part-00000)
    • Solution
  • Lab8: Bike sharing data analysis based on Spark SQL
    • Problem specification (pdf)
    • Skeleton Eclipse project – Spark (Lab8_Skeleton_with_libraries.zip)
    • Basic project based on Maven – Use this version of the project to run the MapReduce application locally on your own PC (DO NOT USE IT AT LAIB1)
      • Import it using Import/Maven/Existing Maven Projects

Exam examples

Pay attention that from the academic year 2020/21 the exam is closed book

  • Exam June 30, 2017
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (b)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam July 14, 2017
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (d)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam September 14, 2017
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (a)
        • Question 2: (b)
        • Source code/Eclipse projects (zip)
  • Exam June 26, 2018
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (c)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam July 16, 2018
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (d)
        • Question 2: (a)
        • Source code/Eclipse projects (zip)
  • Exam September 3, 2018
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (d)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam February 15, 2019
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (d)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam July 2, 2019
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (a)
        • Question 2: (b)
        • Source code/Eclipse projects (zip)
  • Exam July 18, 2019
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (b)
        • Question 2: (b)
        • Source code/Eclipse projects (zip)
  • Exam July 2, 2020
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (b)
        • Question 2: (a)
        • Source code/Eclipse projects (zip)
  • Exam July 16, 2020
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (b)
        • Question 2: (b) – Note that there are two actions and hence the input file is read two times.
        • Source code/Eclipse projects (zip)
  • Exam September 17, 2020
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (d)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam February 5, 2021
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (b)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)
  • Exam June 30, 2021
    • Exam (pdf)
      • Draft of the solution
        • Question 1: (a)
        • Question 2: (c)
        • Source code/Eclipse projects (zip)

Additional material

  • Slides and screencasts about Java (kindly provided by prof. Torchiano) (link)
    • Suggested slides/lectures for those students who have never used Java
      • OO Paradigm and UML (The UML part is not mandatory)
      • The Java Environment
      • Java Basic Features
      • Java Inheritance