Summer School
Lab session files

M2 Research
2017-2018

• Introduction to Map-Reduce
• In-memory processing with Spark
• Stream processing
• Spark lab session (Tweet analysis) and code/data files
• Spark assignment (Flickr analysis) deadline 15th of December
• NoSQL databases
• Neo4j lab session with additional dataset
• Recommender systems
• Pattern mining
• Recommender systems lab session, code & data

Previous years

• Data management in large scale distributed systems: Introduction
• Distributed DBMS Architecture
• Distributed Database design: fragmentation & allocation
• Distributed Query Evaluation
• Background Transactions
• Distributed Transactions
• Replication
• Introduction to MapReduce
• In-Memory Processing with Spark
• Practical work MapReduce: subject and eclipse project (rename to zip)
• NoSQL databases
• Recommender systems 1 2 3 4
• Clustering 1 2 3
• Frequent Itemset Mining
• Spark practical work and skeleton of Spark project (rename it in .zip to decompress)

Mastère Spécialisé Big Data

• Introduction à Map-Reduce
• Sujet TP Hadoop Map Reduce et squelette de projet
• Introduction à Spark
• Streaming
• TP Spark – streaming et squelette de projet
• Recommender systems: sujet, code et données

./fileProducer.sh ../1Mtweets_en.txt .1 | bin/kafka-console-producer.sh --broker-list localhost:9092 --topic batch_tweets
M2 MIAGE / M2 PGI
fileproducer.sh:
#!/bin/sh
while true
do
while read p; do
echo $p
sleep $2
done <$1
done

• Introduction à Map-Reduce
• Sujet TP Hadoop Map Reduce et squelette de projet
• In-memory processing with Spark
• Stream processing
• Spark lab session (Tweet analysis) and code/data files
• debrief Hadoop

Ensimag ISI

• Introduction à Map-Reduce
• Sujet TP Hadoop Map Reduce et squelette de projet et échantillon 1M
• In-memory processing with Spark
• Stream processing
• TP Spark (Tweet analysis) et code/data. Si possible, faites la partie “getting started” avant la séance de TP pour gagner du temps.