PhD Course: Functional Programming for Big Data Processing

Through the pervasive use of computers, smartphones and other digital objects, huge amounts of digital data are generated and collected. This data, commonly referred as Big Data, represents a challenge for the current storage, process, and analysis capabilities. To extract value from such data, novel architectures, programming models and frameworks have been developed in recent years for capturing and analyzing complex and/or high velocity data. The usage of high performance computers, such as Clouds and clusters, paired with parallel and distributed algorithms, are commonly used by data analysts to solve big data problems and obtain valuable information and knowledge in a reasonable time.
This course introduces the most effective programming approaches for Big Data processing. In particular, the functional programming paradigm is introduced and it is discussed how Big Data processing frameworks use it to define scalable and distributed applications. Although functional programming has a long tradition, in the last few years it is becoming very popular, driven by the success of some languages, such as Scala, but also by the adoption of functional programming principles in mainstream languages like Java and Python. The course includes practical examples and real case studies aiming at highlighting how Apache Spark, one of the most popular frameworks for Big Data analysis, exploits functional programming for implementing scalable Big Data analysis applications.

Seminar room -  ground floor, cube 44Z

24/09/2024 - (09:30-11:30)
25/09/2024 - (09:30-11:30)
26/09/2024 - (09:30-11:30)
27/09/2024 - (09:30-11:30)

8ore - 2 CFU