Data Design & Modeling

• big data dimensions: volume, velocity, variety, and veracity
• CRUD primitives (create, read, update, delete) implemented at scale
• ACID/BASE transactional properties
• No-SQL data models and technologies: models, languages, architectures, tools
• sharding and replication strategies
• data analysis pipeline: Acquisition, Integration, Exploration, Mining, Analytics, Interpretation, and Visualization
• data quality, provenance, wrangling, and cleansing to ensure data is worthy of trust
• NOSQL data management technologies, languages, and data models: documental, graph, key-value, columnar, vectorial

Data design and modeling provides the foundation for representing, storing, and managing structured, semi-structured, and unstructured data. Data can be persistent or volatile, processed in batches or continuous streams. Students will learn how to select appropriate data management solutions that address scalability, availability, consistency, performance, and expressiveness requirements. They will learn how to deal with different data models and data management technologies.

• Industry, innovation and infrastructure

In presence

Besides the theory classes, students will experiment with big data technologies through hands-on use cases and practical project activities.

The exam will consist in a written session where theory questions and exercises will be responded to by students on paper. The written exam will account for 60% of the mark. Along with the course, project work activities will be carried out by students in groups. This will count for 25% of the mark. Additionally, 15% of the mark will be earned through quizzes during the course.