1. Theoretical concepts of Big Data
1.1 Complexity Management
1.2 Monitoring and Performance Engineering
1.3 Architectures for Big Data
10 horas
Kleppmann, Martin. Designing Data-Intensive Applications: The Big Ideas behind Reliable, Scalable, and Maintainable Systems. O'Reilly, 2017

2. Distributed and parallel Programming.
2.1 Parallel Programming.
2.2 Map Reduce.
2.3 Distributed file systems.
2.4 Development of applications for execution on Big Data clusters
2.5 Vector Programming.
20 horas
Kleppmann, Martin. Designing Data-Intensive Applications: The Big Ideas behind Reliable, Scalable, and Maintainable Systems. O'Reilly, 2017

3. Applications
3.1 Databases
3.2 Online data analysis
3.3 Machine learning
3.4 Graph analysis
30 horas
Kleppmann, Martin. Designing Data-Intensive Applications: The Big Ideas behind Reliable, Scalable, and Maintainable Systems. O'Reilly, 2017