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Hadoop, Spark, Map Reduce and Blockchain UCA

Enrollment in this course is by invitation only

About This Course


This course will cover two different distributed computing technologies:

The first part, divided into three modules, covers distributed computing in a Big Data context; the course will present a new development paradigm, map/reduce, as well as two frameworks for large-scale parallelized computing on big data sets: Apache Hadoop and Apache Spark.

The course covers the notions of cluster computing, how to conceive algorithms and implement computer programs that can be run on clusters composed of many computers to process and analyze arbitrarily large data sets, and how to execute these programs on clusters using the two leading Big Data computing frameworks, Hadoop and Spark.

Upon completing the course, you will not only be able to deploy computer clusters and create and execute software on them, but also to have a rough handle on the internal architecture of the frameworks and even, to a degree, to expand these frameworks to integrate them with existing software solutions.

The second part, divided into 3 modules, will enable you to learn the basis of blockchains and to be capable of working on a project exploiting this technology.

In the first module, after defining what a blockchain is, you will focus on the bitcoin protocol which is the origin of the blockchain model. You'll study in detail all aspects of the protocol going through the cryptography, the blocks, the transaction, the mining process...

In the second module you'll learn the basics of Smart Contracts by studying the Ethereum protocol. Once again you'll go through the specificities of this protocol and see some examples of code.

In the last module, you'll have to develop your own small and simple blockchain from scratch. This small blockchain project will handle a single public forum where the messages will be stored in blocks and exchanged as transactions.

It's the sixth academic course of the master eMBDS.


Course duration and workload


This is a weekly course over 6 weeks.

Each monday, short video sequences will be offered to participants.

MCQs will evaluate the knowledge at the end of each week.

The weekly MCQs will be used to your self trained.

At the end of the 6 weeks, a supervised exam will be proposed to pass the certificate.

Plan to spend 5h per week + 1h of supervised exam are necessary.


Prerequisites


This self-contained course is integral part of the MBDS Master Graduate Program in Computer Science at the university of Nice – Sophia-Antipolis, in France. Suitable for professionals and students.

For the first part, knowledge of programming is required; as well as basic knowledge of Java & Python.

The second part also requires knowledge of programming and Java; a basic understanding of network protocols is also a plus.



Syllabus

HADOOP SPARK MAP REDUCE AND BLOCKCHAIN is organized into 6 weekly modules :

PART 1 : Hadoop, Spark and map/reduce
  • MODULE - 1
    • Parallel & cluster computing
    • The map/reduce paradigm
      • Description
      • Examples
      • Conclusion
    • Hadoop: general presentation
    • The Hadoop distributed file system
      • Presentation
      • Usage
    • Hadoop: Yarn
  • MODULE - 2
    • Hadoop development
      • The Hadoop Java API
      • The HDFS API
      • Hadoop: more advanced development
      • Examples of implementation
      • Development environment
  • MODULE - 3
    • Apache Hadoop
      • Advanced development: InputFormat, OutputFormat
      • Advanced development: custom Writables
    • Apache Spark
      • Presentation
      • Internal architecture
      • Advantages & conclusion
    • Apache Spark development
      • The Spark API
      • Broadcast variables & accumulators
PART 2 : Blockchain technologies
  • MODULE - 1
    • The bitcoin protocol
  • MODULE - 2
    • Smart Contracts
    • The Ethereum protocol
  • MODULE - 3
    • Creating a custom blockchain


Course Staff

Benjamin RENAUT

Freelance teacher & project manager at University Cote d'Azur

(formerly University of Nice Sophia Antipolis)



Alexandre MAISONOBE

Freelance teacher & project manager at University Cote d'Azur

(formerly University of Nice Sophia Antipolis)



Terms of use

of the course :

Licence Creative Commons BY NC ND : the user must mention the author's name, he may exploit the work except in a commercial context, he can not create a work derived from the original work.

of the content produced by users :

Restrictive license : your production is your intellectual property and can not be reused.

  1. Course Number

    107009
  2. Classes Start

    Jan 14, 2020
  3. Classes End

    Feb 21, 2020
  4. Estimated Effort

    05:00