Distributed architectures for big data processing and analytics (2024/2025)
Courses

Distributed architectures for big data processing and analytics (2024/2025)

General Information

SSD: ING-INF/05

CFU: 8

Professor: Paolo Garza

Teaching Assistants: Simone Monaco and Claudio Savelli

Teaching Material

Introduction
  • Introduction to the course content and exam rules (pdf)
  • Introduction to Big Data (pdf)
  • Big Data Architectures (pdf)
Hadoop and MapReduce
  • Introduction to Apache Hadoop and the MapReduce programming paradigm (pdf)
    • Interaction with HDFS and Hadoop using the command line (pdf)
  • Hadoop implementation of MapReduce (pdf)
    • Source code of the Word Count Ecplise project (WordCount.zip) – Use the import maven project option to import it into Visual Studio Code
    • BigData@Polito environment + Jupyter – How to submit MapReduce jobs on BigData@Polito (pdf)
  • MapReduce – Design patterns – Part 1 (pdf)
  • MapReduce and Hadoop – Advanced Topics: Multiple inputs, Multiple outputs, Distributed cache (pdf)
  • MapReduce – Design patterns – Part 2 (pdf)
  • MapReduce – Relational Algebra/SQL operators (pdf)
Spark
  • Introduction to Apache Spark (pdf)
    • How to submit Spark applications (pdf)
    • 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 (pdf)
    • Key-value RDDs: transformations and actions on key-value RDDs (pdf)
      • Inner join, left outer join, right outer join, full outer join, and “NOT IN” with PairRDDs: Examples – Notebook (JoinsRDD.zip)
    • DoubleRDDs (pdf)
    • Advanced Topics: Cache, accumulators, broadcast variables, custom partitioners, broadcast join (pdf)
  • Spark SQL and DataFrames
  • Data mining and Machine learning algorithms with Spark MLlib
  • GraphX/GraphFrames
    • Introduction to GraphX and GraphFrames (pdf)
    • Graph Algorithms with GraphFrames (pdf)
      • Simple example – Jupyter notebook (GraphFrameExamples.zip)
      • Select kernel GraphFrames (Yarn) to run it on jupyter.polito.it
      • Run “pyspark –packages graphframes:graphframes:0.8.1-spark3.0-s_2.12 –repositories https://repos.spark-packages.org” to run it locally on your PC – Use package graphframes:graphframes:0.8.0-spark2.4-s_2.11 if you locally installed Spark 2 instead of Spark 3
  • Streaming data analytics

Exercises

MapReduce

To configure Visual Studio Code on your laptop, follow these instructions.

Spark

Laboratory Material

No lab activities during the first week.

Team 1: Students from A to K – Tuesday from 11:30 to 13:00 (First lab activity – March 4, 2025) @ LABINF
Team 2: Students from L to Z – Friday from 11:30 to 13:00 (First lab activity – March 7, 2025) @ LABINF

Laptop Configuration

  • MapReduce installation Guide: Configure Visual Studio Code on your laptop (📘guide).
    • Windows users only: You must configure the winutils (🗃️winutils.zip) and set up some environmental variables. Follow this 📘extra guide for the complete configuration.
    • Laboratory materials are available in multiple versions. The version with libraries is the only one you can use on the LABINF computers. Use it on your laptop if you are not interested in running the applications locally. All the other versions are Maven projects, so you can use them locally on your laptop to write the code and then run it locally inside Visual Studio Code or on the BigData@Polito cluster (pay attention that the way you export the jar is different with Maven!). The legend follows: 📚lib: Project/template with libraries, 🐧mavU: Maven project for Linux/MacOS, 🪟mavW: Maven project for Windows (Hadoop projects only).
      • Basic project for MapReduce applications (📚lib, 🐧mavU, 🪟mavW)
  • PySpark Installation Guide: How to run PySpark applications on your PC or Google Colab: You can install PySpark and JupyterLab using Conda/Miniconda/pip (📘guide).

Problem specifications and solutions

Problem specification and input dataSolution
Lab 1: Hadoop and MapReduce
Problem specification (📄pdf)
Basic project and small example dataset (📚lib)
Basic project based on Maven – Use this version to run the MapReduce application locally on your PC (DO NOT USE IT AT LABINF)
— Linux and macOS (🐧mavU)
— Windows (🪟mavW)
— Bigger dataset: finefoods_text.txt (zip)		Solution (Maven-based): Bonus track Lab1_SolBonusMvn.zip
Lab 2: Filter with Hadoop MapReduce
Problem specification (📄pdf)
Skeleton project Hadoop — MapReduce with libraries (📚lib)
Basic Maven project (NOT FOR LABINF) (🐧mavU, 🪟mavW)

Outputs of the first lab (OutputFolderLab1.zip) (OutputFolderLab1BonusTrack.zip). You can use them to test your application locally on your PC.		Solution: Lab2_Sol.zip
Solution Bonus track: Lab2_SolBonus.zip
Lab 3: Frequently bought/reviewed together with Hadoop and MapReduce
Problem specification (📄pdf)
Skeleton project Hadoop — MapReduce (📚lib)
Basic Maven project (NOT FOR LABINF) (🐧mavU, 🪟mavW)

Sample data (📃AmazonTransposedDataset_Sample.txt)		Solution: Lab3_DBD_Sol.zip – This project is based on mvn
— Comments on the three uploaded solutions (pdf)
— The second solution MUST NOT BE USED – It is highly inefficient
Lab 4: Normalized ratings for product recommendations with Hadoop MapReduce
Problem specification (📄pdf)
Skeleton Eclipse project Hadoop – MapReduce (📚lib)
Basic Maven project (NOT FOR LABINF) (🐧mavU, 🪟mavW)

Sample file (📅ReviewsSample.csv)		Solution: Lab4_Sol.zip

Additional material

Slides and screencasts about Java (kindly provided by Prof. Torchiano) (link)
Focus on the following subset of slides/lectures (for students who have never used Java):
— OO Paradigm and UML (The UML part is not mandatory)
— The Java Environment
— Java Basic Features
— Java Inheritance