Distributed big data management
- Durée : 6 semaines
- Effort : 30 heures
- Rythme: ~5 heures/semaine
- Langues: NA
Compétences visées
À la fin de ce cours, vous saurez :
- Mastering a big data architecture with the expected SQL extensions to master N.O.SQL and NEW SQL sytems
- Understanding the expected functionalities of next GQL (Graph Query language) standard
- Mastering the fundamentals of machine learning and deep learning
Description
This course on Distributed big data management encompasses three parts : - Study of SQL extensions to handle any type of data : structured (objects), semi-structured and unstructured data/NO SQL) - Study of Graph Query Languages Primer - Study of distributed processing frameworl to handle big data (hadoop/Spark)
Format
This course last 6 weeks.
Prérequis
Learners should master basic mathematics (linear algebra, graph theory) and basic computer science : SQL (potentially GRADEO on SQL programming) and PYTHON (bachelor level).
Evaluation et Certification
The learner can an exam at the end of each course.
Plan de cours
This course on Distributed big data management encompasses three parts :
I - Study of SQL extensions to handle any type of data : structured (objects), semi-structured and unstructured data/NO SQL).
II - Study of Graph Query Languages Primer.
III - Study of distributed processing framework to handle big data (hadoop/Spark).
I - Study of SQL extensions to handle any type of data : structured (objects), semi-structured and unstructured data/NO SQL).
II - Study of Graph Query Languages Primer.
III - Study of distributed processing framework to handle big data (hadoop/Spark).
Équipe pédagogique
SERGE MIRANDA
Catégories
Professor at University Cote d'Azur (formerly University of Nice Sophia Antipolis)
Thomas Frisendal
Catégories
Graph Data Architect, VIsual Data Modeler, Online Trainer and National Member of the ISO SQL/GQL Committee
