Big Data: Architectures and Data Analytics (2022/2023)
Courses

Big Data: Architectures and Data Analytics (2022/2023)

General Information

SSD: ING-INF/05

CFU: 6

Professor: Daniele Apiletti

Teaching Assistant: Simone Monaco

Q&A teaching assistance on Piazza: piazza.com/polito.it/fall2022/01qydov/

Announcements

  • 20-09-22: The first lecture is scheduled for September 29, 2022 at 13:00 in Classroom R2
  • 26-09-22: No lab activities during the first weeks of the course, Lab will start on October 11th, 2022.
  • We are using Piazza for class discussion, we invite all students to join the course Piazza. Piazza is highly catered to getting help fast and efficiently from both classmates and teachers. Rather than emailing questions to the teaching staff, students are invited to post their questions on Piazza.

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)
    • 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)
    • Spark SQL, Datasets and DataFrames (slides)
    • Data Mining
      • Recap data mining tasks (slides) – From the “Data Science And Database Technology” course
    • Spark MLlib
    • Spark Streaming (slides)
      • Examples: Word Count – Streaming versions (zip)

Exercises

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).

MapReduce

Linux and MacOSWindows
• Basic Eclipse project for MapReduce applications (with libraries) (MapReduceBasicProjectWithLibraries.zip) – Import using Import/General/Existing Projects into Workspace
• Basic Eclipse project for MapReduce applications (based on maven) (MapReduceBasicProject.zip) – Import it using Import/Maven/Existing Maven Projects		• Basic Eclipse project for MapReduce applications (with libraries) (MapReduceBasicProjectWithLibraries.zip) – Import using Import/General/Existing Projects into Workspace

• Setup instructions for running MapReduce applications locally inside Eclipse (ConfigureWindowsEnviroment.pdf)
– You must install also JDK 1.8 and select it for the imported project inside Eclipse. If you already installed the JDK environment but the version is greater than JDK 1.8 you must install also JDK 1.8.
– Winutils executable (winutils.zip)
– Basic Eclipse project for MapReduce applications (based on maven) (MapReduceBasicProjectWindows.zip)

Spark

Laboratory Material

Student GroupTimeRoom
Team A: Students from A to LTue, 16:00-17:30Laib1
Team B: Students from M to ZTue, 17:30-19:00Laib1
  • 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
      • Import it using Import/Maven/Existing Maven Projects
      • Bigger data set: finefoods_text.txt (zip)
  • 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 (zip)

Exam examples

Pay attention that from the academic year 2022/23 the exam is open book.

TextSolutions
Spark Streaming – Examples of multiple choice questions (pdf)Question 1: (c)
Question 2: (d)
Question 3: (b)
Exam June 30, 2017 (pdf)Question 1: (b)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam July 14, 2017 (pdf)Question 1: (d)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam September 14, 2017 (pdf)Question 1: (a)
Question 2: (b)
Source code/Eclipse projects (zip)
Exam June 26, 2018 (pdf)Question 1: (c)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam July 16, 2018 (pdf)Question 1: (d)
Question 2: (a)
Source code/Eclipse projects (zip)
Exam September 3, 2018 (pdf)Question 1: (d)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam February 15, 2019 (pdf)Question 1: (d)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam July 2, 2019 (pdf)Question 1: (a)
Question 2: (b)
Source code/Eclipse projects (zip)
Exam July 18, 2019 (pdf)Question 1: (b)
Question 2: (b)
Source code/Eclipse projects (zip)
Exam July 2, 2020 (pdf)Question 1: (b)
Question 2: (a)
Source code/Eclipse projects (zip)
Exam July 16, 2020 (pdf)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 (pdf)Question 1: (d)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam February 5, 2021 (pdf)Question 1: (b)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam June 30, 2021Exam (pdf)Question 1: (a)
Question 2: (c)
Source code/Eclipse projects (zip)
Exam February 2, 2022 (pdf)Question 1: (b)
Question 2: (d)
Source code/Eclipse projects (zip)
Exam February 21, 2022 (pdf)Question 1: (d)
Question 2: (d)
Source code/Eclipse projects (zip)
Exam July 4, 2022 (pdf)Question 1: (c)
Question 2: (d)
Source code/Eclipse projects (zip)
Exam September 6, 2022 (pdf)Question 1: (c)
Question 2: (d)

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