Data-Intensive-Computing-ID2221

Course contents

• Distributed file systems
• No SQL databases
• Scalable messaging systems
• Big Data execution engines: Map-Reduce, Spark
• High level queries and interactive processing: Hive and Spark SQL
• Stream processing
• Graph processing
• Scalable machine learning
• Resource management.

Intended learning outcomes

The course complements distributed systems courses, with a focus on processing, storing and analyzing massive data. It prepares the students for master projects, and Ph.D. studies in the area of data-intensive computing systems. The main objective of this course is to provide the students with a solid foundation for understanding large scale distributed systems used for storing and processing massive data.

More specifically after the course is completed the student will be able to explain the architecture and properties of the computer systems needed to store, search and index large volumes of data describe the different computational models for processing large data sets for data at rest (batch processing) and data in motion (stream processing) use various computational engines to design and implements nontrivial analytics on massive data explain the different models for scheduling and resource allocation computational tasks on large computing clusters elaborate on the tradeoffs when designing efficient algorithms for processing massive data in a distributed computing setting.