The course provides the foundational concepts and methods for designing, storing, analyzing and managing semi-structured and unstructured data, both in batch and in streaming. The course aims to tame the variety (data in many forms) and velocity (analyzing data streams to enable real-time decisions) dimensions of Big Data, without forgetting the volume dimension.

The variety-oriented part of the course will focus on NoSQL (and not-only-SQL) models and technologies. Students will learn how to select appropriate data management solutions to deal with scalability, availability, consistency, performance and expressiveness requirements.
The course will cover high-level Big Data problems and dimensions, No-SQL data models and technologies (graph, column, document, key-value based storage; persistent and volatile solutions) and design techniques for NoSQL, the transition from ACID to BASE transactional properties, the specification of CRUD primitives (create, read, update, delete) implemented at scale, and the sharding and replication strategies.
The velocity-oriented part of the course will focus on time series, data streams and events both from a deductive and an inductive perspective. The deductive one focuses on domain-specific languages and knowledge representation techniques. Its main goal is to guide the students in exploring the trade-off between usability and rich formal semantics of query languages. The inductive one examines machine-learning problems focusing on massive online learning and, in particular, on the ability to learn when to forget past information.
Finally, the course will cover the basic aspects of the data analysis pipeline: acquisition, integration, exploration, mining, analytics, visualization, and interpretation. 

Students are expected to know the basics about: database management, SQL and distributed system architectures.

The exam consist in a practical part (project work) and a theoretical part (written exam with possible oral discussion). 

The practical part consist in solving a realistic big data engineering problem, based on real or realistic dataset publicly available , accessible via Web API, or provided by the teachers.

The written exam is composed of a mix of theoretical questions regarding any of the course subjects, and excercises, regarding the technical content and how to apply it in practice.

The optional oral examination consists of a discussion about the written test and the practical part of the exam. It can include also questions on any subject of the course.