Complete syllabus here: LIS-4102

• Introduction: dealing with data at scale [slides] [YouTube][YouTube-2]
  • Datification and Data properties
  • Data-centric applications at scale
  • Computing centres: hardware and resources delivery

• Distributed data management and storage 
  • Cluster based data stores [slides][YouTube] [YouTube-2]
    • [MongoExamples] [slides][slides-2][slides-3]
      • Querying: [YouTube-1] [YouTube-2][YouTube-3][YouTube-4]
      • Sharding: [YouTube]
    • Graph databases [slides] [YouTube]
      • Cypher [YouTube]
      • [Neo4JExample]
    • [Polyglot UseCase]
    • Non-functional properties: concurrency, eventual consistency, …
  • Distributed archival systems [slides]
    • Distributed File Systems
    • Data Labs
    • Data Lakes

• Big data processing and analysis Parallel programming models [YouTube] [YouTube][YouTube][YouTube][YouTube] [YouTube][YouTube]
  • Map Reduce: families of algorithms and patterns [slides – part A] [slides – part B][Glossary]
  • Data flow-based models: operators, data representation, management [slides-part A] [slides-part B]
    • Spark programming Use Case [exercise]

• Ecosystems for massive data management and processing
  • Virtualisation [slides][YouTube][YouTube]
  • Containers [slides] [YouTube] [YouTube]
  • High-performance architectures: cluster, HPC, cloud, fog, edge, just in time architectures [slides]

• Perspectives: open problems and trends [slides][YouTube-1][YouTube-2]
  • Data processing and data management divide
  • Data processing workflows: design, test, deployment, and maintenance