Distributed architectures for big data processing and analytics (2022/2023)

Distributed architectures for big data processing and analytics (2022/2023)

This web page is related to an old version of the course.
The web page of the current instance of the course is available at link.

General Information


CFU: 8

Professor: Paolo Garza

Teaching Assistant: Luca Colomba


03-03-2023: Lab activities
— Team 1: Students from A to J – Tuesday from 11:30 to 13:00 (First lab activity – March 7, 2023) @ LABINF
— Team 2: Students from K to Z – Friday from 11:30 to 13:00 (First lab activity – Match 10, 2023) @ LABINF

18-02-2023: The first lecture is scheduled for February 27, 2023, at 8:30 in Classroom 27

Teaching Material

  • 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 by means of 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 in Eclipse
    • PDF version of the code (i.e., PDF version of the java files) (WordCountPDF.zip)
    • 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)
  • 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)
    • 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


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 project jar file).


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