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<!-- <p class="" style="text-align: justify;
text-justify: inter-word;"><span class="dropcap">L</span>e tecnologie dell'informazione ci hanno condotto in un'era dove la produzione,
la condivisione e la fruizione di informazioni fanno ormai parte della quotidianità e di cui spesso siamo attori quasi inconsapevoli:
è ormai pressoché inevitabile non lasciare una traccia digitale di molte delle azioni che compiamo tutti i giorni;
pensiamo ad esempio a contenuti digitali quali foto, video, blog post e a tutto ciò che ruota attorno ai social network (Facebook e Twitter in particolare).
A questo va aggiunto che con “l’internet delle cose”, assistiamo a un incremento di dispositivi come orologi, bracciali,
termostati e mille altri oggetti che sono in grado di connettersi alla rete e quindi generare grandi flussi di dati.
Questa esplosione di dati giustifica la nascita, nel mondo dell’informatica del termine Big Data: esso indica i dati prodotti
in grande quantità, con notevole rapidità e nei formati più diversi, la cui elaborazione richiede tecnologie e risorse che
vanno ben al di là dei sistemi convenzionali di gestione ed immagazzinamento dei dati.
È subito chiaro che, 1) i modelli di memorizzazione dei dati basati sul modello relazionale e,
2) i sistemi di elaborazione basati su stored procedure e su griglie di computazioni non sono applicabili in questi contesti.
Per quanto riguarda il punto 1, gli RDBMS, ampiamente utilizzati per una gran varietá di applicazioni, presentano alcuni
problemi quando la mole dei dati cresce oltre certi limiti. La scalabilità e i costi di implementazione sono soltanto una parte degli svantaggi:
molto spesso, infatti, quando ci si trova di fronte alla gestione di big data, anche la variabilità, ovvero la mancanza di una struttura fissa,
rappresenta una problematica di rilievo. Ciò ha dato una spinta allo sviluppo dei database NoSQL.
I sito web <a href="http://nosql-database.org">NoSql Databases</a> definisce i database NoSQL come
“Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable”.
Questi database sono: distribuiti, open source, scalabili orizzontalmente,
privi di uno schema prefissato (key-value, column-oriented, document-based e graph-based),
facilmente replicabili, privi delle proprietà ACID e in grado di gestire grandi moli di dati.
Questi database sono integrati o integrabili con strumenti di elaborazione basati sul paradigma MapReduce proposto da Google nel 2009.
MapReduce con il framework open source Hadoop rappresentano il nuovo modello per l’elaborazione distribuita di grandi moli di dati
che va a soppiantare tecniche basate su stored procedure e griglie computazionali (punto 2).
Il modello relazionale insegnato nei corsi di base di progettazione di database,
presenta molteplici limiti rispetto alle esigenze poste dalle nuove applicazioni
basate sui Big Data e che utilizzano database NoSQL per memorizzare i dati e MapReduce per elaborare grosse moli di dati.
</p> -->
<p class="" style="text-align: justify;
text-justify: inter-word;"><span class="dropcap">T</span>
he Information Technology have led us into an era where the production,
sharing and use of information are now part of everyday life and of which
we are often unaware actors almost: it is now almost inevitable not leave
a digital trail of many of the actions we do every day; for example, by
digital content such as photos, videos, blog posts and everything that
revolves around the social networks (Facebook and Twitter in particular).
Added to this is that with the "internet of things", we see an increase in
devices such as watches, bracelets, thermostats and many other items that
are able to connect to the network and therefore generate large data streams.
This explosion of data justifies the birth, in the world of the term Big Data:
it indicates the data produced in large quantities, with remarkable speed and
in different formats, which requires processing technologies and resources that
go far beyond the conventional systems management and storage of data.
It is immediately clear that, 1) models of data storage based on the relational model,
and 2) processing systems based on stored procedures and computations on grids are not
applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great
variety of applications, have some problems when the amount of data grows beyond certain
limits. The scalability and cost of implementation are only a part of the disadvantages:
very often, in fact, when there is opposite to the management of big data, also the variability,
or the lack of a fixed structure, represents a significant problem. This has given a boost
to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases
such as "Next Generation Databases mostly addressing some of the points: being non-relational,
distributed, open source and horizontally scalable." These databases are: distributed,
open source, scalable horizontally, without a predetermined pattern
(key-value, column-oriented, document-based and graph-based), easily replicable,
devoid of the ACID and can handle large amounts of data. These databases are integrated or
integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009.
MapReduce with the open source Hadoop framework represent the new model for distributed processing
of large amounts of data that goes to supplant techniques based on stored procedures and computational
grids (step 2). The relational model taught courses in basic database design, has many limitations
compared to the demands posed by new applications based on Big Data and NoSQL databases that use to
store data and MapReduce to process large amounts of data.
</p>
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<div class="span6">
<h3>Questo insegnamento mira a</h3>
<ul class="check-list">
<li>introdurre i diversi modelli di database NoSQL</li>
<ul class="unstyled">
<li class="inner-li">Key-Value: <a href="http://riak.basho.com/">Riak</a>, <a href="http://redis.io/">Redis</a>, <a href="http://www.aerospike.com/">Aereospike</a>, <a href="https://github.com/google/leveldb">LevelDB</a></li>
<li class="inner-li">Colum-Oriented: <a href="http://hbase.apache.org/ca">HBase</a>, <a href="http://cassandra.apache.org/">Cassandra</a> e <a href="http://hypertable.com/">Hypertable</a></li>
<li class="inner-li">Document-Oriented: <a href="http://www.mongodb.org/">MongoDB</a>, <a href="http://couchdb.apache.org/">CouchDB</a>, <a href="http://www.couchbase.com/">CouchBase</a>, <a href="http://www.rethinkdb.com/">RethinkDB </a> </li>
<li class="inner-li">Graph-Based: <a href="http://neo4j.com/">Neo4J</a>, <a href="https://github.com/thinkaurelius/titan">TitanDB</a> , <a href="http://www.orientechnologies.com/">OrientDB</a> e <a href="https://github.com/twitter/flockdb">Twitter FlockDB</a></li>
<li class="inner-li">Index-Based: <a href="https://www.elastic.co/">Elasticsearch</a></li>
</ul>
<li>sviluppare competenze di progettazione e sviluppo di basi di dati con modelli di nuova generazione basati su NoSQL</li>
<ul class="unstyled">
<li class="inner-li">Data Model</li>
</ul>
<li>sviluppare competenze per l’elaborazione di Big Data utilizzando MapReduce and Spark</li>
<li>setup di servizi elastici e scalabili: <a href="https://www.docker.com/">Docker</a>, <a href="https://www.vagrantup.com/">Vagrant</a> e <a href="http://mesosphere.io/">Mesos</a></li>
<li>sviluppare codice in team: <a href="https://try.github.io/levels/1/challenges/1">Git</a>, <a href="http://www.scala-sbt.org/">SBT</a> e <a href="http://maven.apache.org/">Maven</a></li>
<li>Bere tanto caffè <i class="fa fa-smile-o"></i></li>
</ul>
</div>
<div class="span6">
<h3>Competenze</h3>
<div class="skill">
<h6 class="name">Data Bases Design</h6>
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<h6 class="name">NoSQL Databases</h6>
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<h6 class="name">Big Data</h6>
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<h6 class="name">Database as (Micro) Service</h6>
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<h6 class="name">Data Analytics</h6>
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<h6 class="name">Docker containers</h6>
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<h6 class="name">Sbt, Maven</h6>
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<h6 class="name">Scala Lang</h6>
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<h3 class="title">Aula 1A - first floor at Department of Computer Science Uniba</h3>
<h4 class="title">- Tuesday (Martedì) from 9:00 to 12:00</h4>
<h4 class="title">- Thursday (Giovedì) from 15:00 to 17:00 (Bring your laptop)</h4>
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<h2 class="section-title">Instructor</h2>
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<img src="assets/img/lazy_team.png" data-original="images/team/fabio.jpg" alt="Fabio Fumarola">
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<span class="name"><script type="text/javascript">linkedin('pub/fabio-fumarola/7/540/605', 'Fabio Fumarola')</script></span>
<span class="role">Instructor</span>
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Dottore di Ricerca in Informatica. Esperto in Web Mining e Big Data; Analista, Progettista e Sviluppatore Software. Esperto in NoSQL.
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Dottore di Ricerca in Informatica. Esperto in Web Mining e Big Data; Analista, Progettista e Sviluppatore Software. Esperto in NoSQL.
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<div class="title">Personal Information</div>
<ul>
<li>Email: [email protected], [email protected]</li>
<li>Phone: 080 544 32 69</li>
<li>Address: Via E. Orabona 4, Department of Informatics, 5 floor room 509</li> <!-- -->
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<hr>
<div class="title">Technical Skills</div>
<ul>
<li>Operating systems: Linux (mostly Ubuntu), Mac Os X</li>
<li>Main programming languages: Scala, Java</li>
<li>Other programming, scripting and query languages: SQL, Ruby, HTML/XML, CSS, Node.js, R, Python</li> <!-- -->
<li>Frameworks and libraries: Akka.io, JUnit, Apache Hadoop, Apache Spark, Play2, AngularJS,... </li>
<li>Other software: Maven3, Git, SBT, Gradle, Perforce, Hansoft, Apache Tomcat, Eclipse, Netty, ...</li>
<li>Other Tools: NLTK, Weka, Moa, HBase, TitanDB, Docker, Vagrantx</li>
</ul>
<div class="title">Repositories</div>
<ul>
<li><a href="https://github.com/fabiofumarola">Github</a></li>
<li><a href="https://bitbucket.org/fabiofumarola">Bitbucket</a></li>
<li><a href="http://www.slideshare.net/fabiofumarola1">Slideshare</a></li>
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<h3 class="title">1. Course Introduction (first week)</h3>
<p> Topics: evolution of enterprise computing, from business to decision support (`60, `80, `90, `2000), scaling up databases, data variety, connectivity,
P2P knowledge, concurrency, cloud, RDBMS issues, NoSQL databases intro, impedance mismatch, attack of the cluster</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/NzJ7xT0gMoHEk" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<a href="http://www.slideshare.net/fabiofumarola1/1introduction-46283802" onClick="_gaq.push(['_trackEvent', 'slides', 'download', '1.Introduction']);"><span class="label label-success">Slides Download</span></a>
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<h3 class="title">2. Linux Containers and Docker (second week)</h3>
<p> Topics: The Evolution of IT, The Solutions: Virtual Machines vs Vagrant vs Docker, Differences,
Examples: Vagrant, Boot2Docker, Docker, Docker Hub,
Orchestrate Docker, Mesosphere e CoreOS</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/HrY1aUNR8roNZ2" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<h3 class="title">3. An Introduction to Git (second week)</h3>
<p> Topics: What is Version Control? (and why use it?), What is Git? (And why Git?), How git works
Create a repository, Branches, Add remote, How data is stored </p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/bry0vPcdJcZqXk" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<h3 class="title">4. How to manage dependencies (third week)</h3>
<p> Topics: How to create a java project without an IDE, How do to manage dependencies on a standard way, How to execute task to build a project</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/KmtJHFEiieAQTt" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<h3 class="title">5. NoSQL based Data Models</h3>
<p> Topics: Data Model Evolution, Relational Model vs Aggregate Model, Consequences of Aggregate Models ,Aggregates and Transactions,
Aggregates Models on NoSQL, Key-value and Document, Column-Family Stores, Summarizing Aggregate-Oriented databases
</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/q4XZebKvTdVKnh" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<a href="readingMaterial/Evans03.pdf" onClick="_gaq.push(['_trackEvent','slides', 'download', '5.NoSQL']);"><span class="label label-success">Domain-Driven Design</span></a>
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<h3 class="title">6. More on NoSQL based Data Models</h3>
<p> Topics: How to deal with relationships – Graph Databases, Materialized Views, Modeling for Data Access, Distribu0on Models (Single server, Sharding,
Master-Slave, Peer-to-Peer)
</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/Cjd9TdFbJA3uoQ" width="425" height="355" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" style="border:1px solid #CCC; border-width:1px; margin-bottom:5px; max-width: 100%;" allowfullscreen> </iframe>
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<h3 class="title">7. Key-Value Data Store and Case Study</h3>
<p> Topics:Key-values introduction,Major Key-Value Databases, Dynamo DB: How is implemented, Background,
Partitioning: Consistent Hashing, High Availability for writes: Vector Clocks,
Handling temporary failures: Sloppy Quorum, Recovering from failures: Merkle Trees,
Membership and failure detection: Gossip Protocol
</p>
<iframe src="//www.slideshare.net/slideshow/embed_code/key/8Y3WWTYxuDBhJ1" width="476" height="400" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe>
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<h3 class="title">8. Column-Oriented Data Store and Case Study</h3>
<p> Topics:bigtable, cassandra, column-oriented, design nosql databases, hbase, hypertable, immutability, nosql, SSTable, tablet server.
</p>
<iframe src="https://www.slideshare.net/slideshow/embed_code/key/dKWUitWasPjXvR" width="476" height="400" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe>
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<h3 class="title">9. Document-Oriented Database in depth</h3>
<p> Topics:Introduction, What is a Document, DocumentDBs, MongoDB, Data Model, Indexes, CRUD, Scaling, Pros and Cons.
</p>
<iframe src="https://www.slideshare.net/slideshow/embed_code/key/3dW7Wxc9d1ZvE6" width="476" height="400" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe>
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<h3 class="title">10. Graph-Oriented Database</h3>
<p> Topics: Introduction, The Lack of relationship for RDBMS and NoSQL, Graph Databases: Features, Relations,
Query Language, Data Modeling with Graphs and Conclusions
</p>
<iframe src="https://www.slideshare.net/slideshow/embed_code/key/dSTh3PwSLsHr69" width="476" height="400" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe>
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<h3 class="title">11. From Hadoop to Spark 1/2</h3>
<p> Topics:Aggregate and Cluster, Scatter Gather and MapReduce, MapReduce , Why Spark?, Spark (Example, task and stages),
Docker Example, Scala and Anonymous Functions, Next Topics in 2/2
</p>
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<h3 class="title">Introduction to AngularJS + demo</h3>
<p> General introduction to Single Page Applications using AngularJS with a final demo. Thanks to Nicola Sanitate and Francesco Abbattista
</p>
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<h3 class="title">11. From Hadoop to Spark 2/2</h3>
<p> Topics:spark-shell, pyspark, HDFS, how to copy file to HDFS, spark transformations, spark actions, Spark SQL (Shark),
spark streaming, streaming transformation stateless vs stateful, sliding windows, examples
</p>
<iframe src="https://www.slideshare.net/slideshow/embed_code/key/N995r3FmZdp2CH" width="476" height="400" frameborder="0" marginwidth="0" marginheight="0" scrolling="no"></iframe>
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<h3 class="title">Recommended Books</h3>
<h4> <a href="http://martinfowler.com/books/nosql.html" >NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence</a> </h4>
<h4> <a href="http://www.amazon.it/Architettura-tecnologie-metodi-lutilizzo-grandi/dp/8838789894">Big data. Architettura, tecnologie e metodi per l'utilizzo di grandi basi di dati </a> </h4>
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<h3 class="title">Suggested Books</h3>
<h4> <a href="http://www.manning.com/marz/" >Big Data: principles and best practices of scalable realtime data systems</a> </h4>
<h4> <a href="http://www.manning.com/dimidukkhurana/">HBase in Action</a></h4>
<h4> <a href="http://shop.oreilly.com/product/0636920021773.do">Hadoop: the Definitive Guide</a></h4>
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