Massive Data Processing

CPE Lyon

Academic Year 2025-2026

Master the art of processing, analyzing, and extracting insights from massive datasets using distributed computing frameworks, data mining algorithms, and modern big data technologies including Hadoop, Hive, and Spark.
HUB S1 S2 S3 P1 P2 P3 DISTRIBUTED PROCESSING

Course Overview

Syllabus

Course Content & Structure

Complete syllabus outlining all topics covered in this course.

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Course Materials

Introduction

Introduction to Big Data

Comprehensive overview of learning objectives, assessment criteria, and detailed schedule of lectures and practical sessions throughout the academic year. Comprehensive overview of big data concepts, characteristics (Volume, Velocity, Variety), challenges, and the ecosystem of tools and technologies for massive data processing.

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Fundamentals

Data Representation & Processing

Data structures, manipulation techniques, processing paradigms, and analytical methods for handling datasets.

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Data Mining

Patterns & Mining Algorithms

Pattern recognition, classification, clustering, association rules, and advanced data mining algorithms for knowledge discovery.

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AI & Ethics

Neural Networks & Data Ethics

Artificial neural networks, their applications in massive data contexts, and ethical considerations in data usage and privacy.

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Semantic Web

Linked Open Data

Semantic web technologies, RDF, SPARQL, and leveraging linked open data for enhanced data integration and interoperability.

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Big Data Tools

Hadoop, Hive & Spark

Hands-on with industry-standard big data frameworks: distributed storage with Hadoop, data warehousing with Hive, and fast processing with Apache Spark.

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Practical Sessions

GitHub Repository

TDM Repository

Complete practical exercises covering data processing pipelines, mining algorithms implementation, Hadoop/Spark projects, and real-world big data analysis scenarios. Includes datasets, code templates, and comprehensive bilingual tutorials.

Access on GitHub