This module explores transaction management in distributed database systems, focusing on concurrency control methods. You will learn to identify ACID properties to maintain database consistency, develop transaction plans with operations and partial orderings, and implement various concurrency control and deadlock management algorithms, including two-phase locking and time-based techniques.

This module explores reliability protocols in distributed databases, focusing on maintaining consistency and durability during system failures. Key recovery and reliability protocols, including ARIES, two-phase, and three-phase commit, are covered. In addition, students will gain foundational knowledge of data warehousing principles, along with an introduction to Accumulo architecture. This includes basic Accumulo functionalities and cell-level security mechanisms essential for large-scale distributed data management.

This module introduces core cloud computing principles with a focus on the Hadoop ecosystem and its utility for large-scale data processing. Emphasizing the MapReduce framework, learners will explore pseudocode development and architecture. The module also integrates foundational machine learning concepts, specifically clustering, classification, and collaborative filtering algorithms using Mahout and Accumulo. These techniques equip learners to perform scalable data analysis and build recommendation systems within Hadoop, suitable for managing and analyzing large datasets.