Big Data Processing and Analytics (2024/25)
Courses

Big Data Processing and Analytics (2024/25)

General Information

SSD: ING-INF/05

CFU: 6

Professor: Paolo Garza

Teaching Assistant: Luca Colomba

Announcements

Lecture schedule: November 18 – November 22

Monday, November 18, 14:30-17:30On-site lecture (Room R1) + streaming virtual classroom
Friday, November 22 – 14:30-16:00Team 1 – LAIB3 + streaming virtual classroom
Friday, November 22 – 16:00-17:30Team 2 – LAIB3 + streaming virtual classroom

Team 1: Students from A to K – Friday from 14:30 to 16:00 – LAIB3

Team 2: Students from L to Z – Friday from 16:00 to 17:30 – LAIB3

Teaching Material

INTRODUCTION
  • Introduction to the course content and exam rules (slides) – Updated on September 23, 2024
  • Introduction to Big Data (slides) – Updated on September 23, 2024
  • Big Data Architectures (slides)
HADOOP AND MAPREDUCE
  • Introduction to Apache Hadoop and the MapReduce programming paradigm (slides) – Updated on September 23, 2024
    • 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) – Counters are not covered this year.
  • 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)
    • How to use Jupyter Notebooks for your Spark applications (pdf)
    • You can install PySpark and JupyterLab using Conda/Miniconda/pip (instructions here)
  • RDD-based programs RDDs
    • Creation, basic transformations, and actions (slides) – Notebook with some examples from the slides (FirstExamplesNotebook.zip) – Examples uploaded on October 28
    • Key-value pair RDDs: transformations and actions on PairRDDs (slides)
      • Inner join, left outer join, right outer join, full outer join, and “NOT IN” with PairRDDs: Examples – Notebook (JoinsRDD.zip)
    • DoubleRDDs (slides)
    • Advanced Topics: Cache, accumulators, broadcast variables (slides) – Notebooks with some examples (ExamplesAccumulatorPython.zip) – Examples uploaded on November 18
  • Spark SQL, Datasets, and DataFrames (slides)
  • Spark MLlib
    • Introduction to MLlib (slides)
    • Classification of structured data and textual data (slides)
      • Classification example code (zip)
    • Regression (slides)
      • Linear regression 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)
  • Spark Streaming (slides)

Exercises

MAP REDUCE
  • MapReduce exercises (slides)
  • How to Write and Compile your Java Application using VSCode (pdf)
  • Linux or Mac: Basic project for MapReduce applications (based on maven) (MapReduceBasicProject.zip)
  • Windows: Basic project for MapReduce applications (based on maven) (MapReduceBasicProjectWindows.zip)
    • You must also install JDK 1.8 and select it for the imported project inside the IDE. If you have already installed the JDK environment but the version is greater than JDK 1.8, you must also install JDK 1.8.
    • Winutils executable (winutils.zip)
  • If you use your PC to write and run your code locally, use the projects based on Maven (those projects can be run locally).
  • If you use the PC available in the LAB, import the projects with libraries as reported in the first lab (those projects cannot be run locally but only on the cluster exporting the project jar file).
SPARK

Laboratory Material

Team 1: Students from A to K – Friday from 14:30 to 16:00 – LAIB3

Team 2: Students from L to Z – Friday from 16:00 to 17:30 – LAIB3

  • How to Write and Compile your Java Application using VSCode (pdf)
Problem specification and input dataSolution (Maven-based for Java)
Lab 1: Hadoop and Map Reduce
Problem specification (pdf)
Basic project and small example dataset (Lab1_BigData_with_libraries_vscode.zip)
Basic project based on Maven – Use this version to run the MapReduce application locally on your own PC (DO NOT USE IT AT LAIB3)
— Linux and macOS (Lab1.zip)
— Windows (Lab1_Windows.zip)
Bigger dataset: finefoods_text.txt (zip)		Solution: Bonus track Lab1_SolBonusMvn.zip
Lab 2: Filter with Hadoop MapReduce and Frequency Count
Problem specification (pdf)
Skeletion project Hadoop — MapReduce (Lab2_Skeleton_with_libraries_vscode.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 LAIB3)
— Linux and macOS (Lab2_Skeleton.zip)
— Windows (Lab2Windows_Skeleton.zip)
Outputs of the first lab (OutputFolderLab1.zip). You can use them to test your application locally on your own PC if you are using Maven		Solution: Task 1, Task 2
Lab 3: Frequently bought/reviewed together with Hadoop and MapReduce
Problem specification (pdf)
Skeleton project Hadoop — MapReduce (Lab3_Skeleton_with_libraries_vscode.zip)
Sample data (AmazonTransposedDataset_Sample.txt)
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 LAIB3)
— Linux and macOS (Lab3_Skeleton.zip)
— Windows (Lab3Windows_Skeleton.zip)		Solution: Lab3_Sol.zip – The second solution MUST NOT BE USED because it is highly inefficient. The second solution has been uploaded to show an inefficient solution that someone implemented in the past.
Comments on the three uploaded solutions (slides)
Lab 4: Normalized ratings for product recommendations with Hadoop MapReduce
Problem specification (pdf)
Skeleton project Hadoop — MapReduce (Lab4_Skeleton_with_libraries_vscode.zip)
Sample file (ReviewsSample.csv)
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 LAIB3)
— Linux and macOS (Lab4_Skeleton.zip)
— Windows (Lab4Windows_Skeleton.zip)		Solution: Lab4_Sol.zip
Lab 5: Filter data and compute basic statistics with Apache Spark
Problem specification (pdf)
Sample file (SampleLocalFile.csv)		Solution: Lab5_Sol.zip
Lab 6: Frequently bought/reviewed together with Apache Spark
Problem specification (pdf)
Sample file (ReviewsSample.csv)

Expected output – Task 1 (expected output if the input is the HDFS file Reviews.csv) (outputTask1Lab6.zip)		Solution: …

Previous exam examples

The Spark solutions of some past exams are still based on Java. They will be updated to Python in the coming weeks. However, except for the syntax, the solutions are based on the same Spark methods and workflows. The solutions are programming language-independent.

ExamsSolutions
Spark Streaming – Examples of multiple choice questions (pdf)Question 1: (c)
Question 2: (d)
Question 3: (b)
June 20, 2024: pdfQuestion 1: (b)
Question 2: (a)
Source code: zip – Spark – Python-based solution
February 19, 2024: pdfQuestion 1: (a)
Question 2: (b)
Source code: zip – Spark – Python-based solution
February 5, 2024: pdfQuestion 1: (a)
Question 2: (b)
Source code: zip – Spark – Python-based solution
A solution based on Spark SQL is available for this exam
September 21, 2023: pdfQuestion 1: (a)
Question 2: (b)
June 21, 2023: pdfQuestion 1: (c)
Question 2: (d)
February 15, 2023: pdfQuestion 1: (c)
Question 2: (b)
Source code: zip – Spark – Python-based solution
A solution based on Spark SQL is available for this exam
February 2, 2023: pdfQuestion 1: (d)
Question 2: (d)
Source code: zip – Spark: a solution based on Spark SQL is available for this exam
September 6, 2022: pdfQuestion 1: (c)
Question 2: (d)
Source code: zip – Spark: a solution based on Spark SQL is available for this exam
July 4, 2022: pdfQuestion 1: (c)
Question 2: (d)
Source code: zip
February 21, 2022: pdfQuestion 1: (d)
Question 2: (b)
Source code: zip – Spark: a solution based on Spark SQL is available for this exam
February 2, 2022: pdfQuestion 1: (b)
Question 2: (d)
Source code: zip
June 30, 2021: pdfQuestion 1: (a)
Question 2: (c)
Source code: zip
February 5, 2021: pdfQuestion 1: (b)
Question 2: (c)
Source code: zip
September 17, 2020: pdfQuestion 1: (d)
Question 2: (c)
Source code: zip
July 16, 2020: pdfQuestion 1: (b)
Question 2: (b) – Note that there are two actions; hence, the input file is read twice.
Source code: zip
July 2, 2020: pdfQuestion 1: (b)
Question 2: (a)
Source code: zip
July 18, 2019: pdfQuestion 1: (b)
Question 2: (b)
Source code: zip
July 2, 2019: pdfQuestion 1: (a)
Question 2: (b)
Source code: zip
February 15, 2019: pdfQuestion 1: (d)
Question 2: (c)
Source code: zip
September 3, 2018: pdfQuestion 1: (d)
Question 2: (c)
Source code: zip
July 16, 2018: pdf
Question 1: (d)
Question 2: (a)
Source code: zip
June 26, 2018: pdfQuestion 1: (c)
Question 2: (c)
Source code: 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 can be skipped)
      • The Java Environment
      • Java Basic Features
      • Java Inheritance