Course: Big Data analysis with Hadoop and RHadoop

19 Oct 2022, 13:00 → 20 Oct 2022, 17:05 Europe/Ljubljana

Zoom Meeting

This training course will focus on the foundations of “Big Data” processing by introducing the Hadoop distributed computing architecture and providing an introductory level tutorial for Big Data analysis using Hadoop, Rhadoop, and R libraries parallel, doParallel, foreach and Rmpi. Although online, the course will be hands-on, allowing participants to work interactively on real data on the High Performance Computing environment of the University of Ljubljana and on the Vienna Scientific Cluster.

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Organizers

   

This course is an EuroCC event jointly organised by EuroCC Slovenia, EuroCC Slovakia and EuroCC Austria.

 

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Lecturers

• Prof. Janez Povh, University of Ljubljana, Slovenia (applied mathematics, high performance computing, big data analysis)
• Lucia Absalon Bautista, University of Ljubljana, Slovenia (big data analysis)
• Dr. Giovanna Roda, EuroCC Austria, BOKU, and TU Wien, Austria (high performance computing, big data analysis)
• Liana Akobian, TU Wien, Austria (high performance computing, big data analysis)

 

Course Material

NoMachine - HPCFS login.pdf

Wednesday, 19 October

Introduction

Introduction to HADOOP

Introduction to Big Data
The Hadoop Distributed Computing Architecture
First hands-on excercise on the cluster

Break

HDFS

The Hadoop Distributed File System: blocks, partitions, load balancing, replication/erasure coding, fault tolerance, data locality
Hands-on example: managing data on HDFS

Break

MapReduce (MR)

Explaning the MR computing model
Split/ map/ sort & shuffle/combine/reduce
Hands-on demos

Break

Hands-on exercise with MR

Thursday, 20 October

Introduction to Day 2

Introduction to R

Connecting to RStudio web server at HPC@UL
Creating and running own R scripts
Creating, retrieving, saving data files
Standard data management operations on data frames
Data management with dplyr, magritt

Big_Data_and_RHadoop_slides_october 2022.pdf

Break

Advanced and Big data management with R

Dana manipulations with apply functions apply, lapply, sapply, vapply, tapply and mapply
Big Data management and analysis using one computing node with functions for efficient parallel loops parLapply, parSapply, mcLapply and foreach-dopar

Break

Big Data management and analysis with Rmpi and RHadoop

Big Data management and analysis using many computing nodes and library Rmpi
Preparing and storing big data to HDFS using rhdfs library
Retriving from and managing big data in HDFS by plyrmr and rhdfs library

Break

Big data analysis with RHadoop

Preparing map-reduce scripts to make basic data analysis tasks (extreme values, counts, mean values, dispersions, visualisations) using rhdfs library

Wrap-up