This curricular unit will provide knowledge on tools for storage, processing and visualization of large volumes of data, the development of skills in the construction and testing of efficient algorithms for Big Data, namely the study of paradigms, models, tools and parallel programming languages.
At the end of the course the student should be able to

- Determine the solution to be applied and the instruments to be used in the storage, exploration and analysis of a large volume of data
- Select appropriate visualization options to summarize and extract knowledge from a large volume of data
- Understand the concept of parallel and distributed processing as a way to increase performance in data management and analysis

- Develop algorithms and models to solve problems that explore the management of concurrency, distribution and parallelism
- Recognize the different hardware architectures that support the operation of these algorithms

1. Visualization of large data volumes
2. Large-scale storage
Non-relational databases (key-value, document-oriented, column family, graph-oriented) Comparison between relational and non-relational databases
3. Parallel Programming Models
Shared Memory Model
Thread Model
Distributed memory
Message passing model
Parallel Data Model
Hybrid model
Single Program Multiple Data (SPMD)
Multiple Program Multiple Data (MPMD)
4. Design of parallel programs
Automatic parallelization vs. Manual
Partitioning
Communications
Synchronization
Data dependencies

The predominant teaching methodologies will be the presentation of concepts, using slides and the demonstration of examples in the computer laboratory. Students will be constantly challenged to solve new problems, based on the examples already demonstrated, and to reflect on the results and performance of the storage and processing processes under study.