M2 Data management in large-scale distributed systems

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The DataScale programme focuses on the study of and proficiency in new data management and knowledge extraction architectures, from the very large data servers (data centres) to our countless connected objects (edge computing). It aims to provide students with a precise understanding of the technological and scientific challenges involved in the design of these new architectures and the development of Big Data applications using them. The complexity, heterogeneity, and distribution of data and processing must be taken into account as much as the reliability, security, and performance of the underlying data managers (RDBMS or NoSQL).


The skills acquired at the end of the programme follow three axes:

1) big data architectures, cloud and IoT, security and performance;

2) data integration and quality, data mining and knowledge extraction;

3) development and deployment of data-driven services and applications.

Students receive a common core curriculum along these three axes. This common core is supplemented by optional courses covering various topics such as data mining, data confidentiality, the semantic web, cloud computing, ambient data management, the Internet of Things or application development frameworks, opening up research perspectives associated with these different topics.

Course Prerequisites

The DataScale study path is available only as an introductory programme. As a prerequisite, students must have the equivalent of a Master 1 level in French Informatics, with a solid understanding of databases. The typical profiles of students joining DataScale programme are: students who have obtained a Master's degree in IT in France, students from partner engineering schools doing their last year in a dual course, students who hold an engineering or Informatics Master's degree obtained outside of France.

  • Deploy, use and manage a large-scale data management infrastructure.

  • Extract, analyse and exploit the information and knowledge stored in a large-scale data management infrastructure.

  • Develop and deploy service-oriented data management applications.

  • Produce and present an overview report and carry out a scientific approach.

Post-graduate profile

At the end of the programme, students will show proficiency in the three main skills directly linked to the three pillars of the training, namely:
-Deploy, use and manage a large-scale data management infrastructure
-Extract, analyse and exploit the information and knowledge stored in a large-scale data management infrastructure
-Develop and deploy service-oriented data management applications
Students must also be able to produce and present an overview report and carry out a scientific approach in response to a given subject.

These skills are acquired through core common curriculum teaching units and optional units that give students targeted academic knowledge supplemented by the empirical knowledge acquired in carrying out projects; through seminars that introduce students to the topics and practices relating to the worlds of research, industry and services; and finally, through an internship of long duration, to apply acquired knowledge to the reality on the field.

Career prospects

The programme leads students towards management and senior management careers in IT in industry and services, and research-based careers and R&D posts in data management at universities, in private and public research organizations, large companies or start-ups. After obtaining the Master's degree, graduates may continue their studies and apply for a doctoral programme.

Students will be particularly equipped to take up jobs in: database administration (DBA), information systems security administration (DSA), data analysis (data scientists), urbanisation of information systems, design and deployment of empirical knowledge and distributed cations, etc.


Inria Saclay-Île-de-France.

Données et Algorythmes pour une Ville Intelligente et Durable
Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux.


Le semestre 1 est composé d'un Tronc Commun (6 UE) et d'Options (6 UE au choix).

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Qualité des données 2.5 21
Modèles et éco-systèmes Post-Relationnels 2.5 15 6
Machine Learning 2.5 21
Intégration de données 2.5 21
Architectures orientées Services 2.5 15 12
Architectures des gestionnaires de données 2.5 18 3
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Web sémantique 2.5 21
Sûreté, Sécurité Informatique et Fiabilité 2.5 21
Sécurité des données corporate et personnelles 2.5 21
Modélisation de processus métiers 2.5 15 6
Gestion et analyse de données spatio-temporelles 2.5 21
Gestion de données et de services dans le cloud 2.5 15 6
Gestion de données ambiantes et internet des objets 2.5 21
Frameworks pour le développement d'applications Web avancées 2.5 15 6
Fouille de données et analyse prédictive 2.5 12 12

Le second semestre est composé principalement d'un stage de 5 mois ainsi que d'un groupe d'UE professionnalisantes composé d'une UE d'anglais, une UE de connaissance de l'entreprise, une UE de séminaires industriels/recherche et d'un projet annuel.

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Séminaires Industriels et Recherche 1 20
Projet Programmation 1.5 1.5
Projet Conception 1.5 1.5
Connaissance du monde du travail 2 21
Anglais 3 27
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Stage 21 2
Modalités de candidatures
Application period
From 01/02/2021 to 16/07/2021
Compulsory supporting documents
  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Curriculum Vitae.

  • Certificate of French (compulsory for non-French speakers).

Additional supporting documents
  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

Course manager(s)
Philippe PUCHERAL - philippe.pucheral@uvsq.fr
Zoubida Kedad-Cointot - zoubida.kedad@uvsq.fr
Administrative office
Fabienne Chevalier - Fabienne.Chevalier@uvsq.fr