What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Large-Scale Database Systems Specialization
Instructor: David Silberberg
Included with
Recommended experience
Intermediate level
A basic understanding of distributed databases, cloud computing, machine learning concepts, SQL and programming is recommended.
Recommended experience
Intermediate level
A basic understanding of distributed databases, cloud computing, machine learning concepts, SQL and programming is recommended.
Learn transaction management principles, including ACID properties, concurrency control, and deadlock management techniques for distributed systems.
Explore reliability protocols, recovery algorithms, and commit protocols like ARIES, ensuring data consistency and durability.
Understand cloud computing with Hadoop, utilizing MapReduce for large-scale data processing, and apply machine learning techniques like clustering.
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December 2024
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The course "Reliability, Cloud Computing and Machine Learning" explores advanced distributed database concepts, focusing on transaction management, reliability protocols, and data warehousing, while also diving deeper into cloud computing and machine learning. You will develop a solid understanding of transaction principles, concurrency control methods, and how to ensure database consistency during failures using ACID properties and protocols like ARIES. The course uniquely integrates Hadoop, MapReduce, and Accumulo, offering hands-on experience with large-scale data processing and machine learning applications such as collaborative filtering, clustering, and classification.
By mastering these advanced topics, you'll gain the skills necessary to work with cutting-edge technologies used in cloud-based data processing and scalable machine learning analysis. With practical applications in both reliability management and machine learning, this course prepares you to tackle complex data management challenges, making you well-equipped for careers in cloud computing, distributed systems, and data science.
This course examines advanced distributed database topics, focusing on transaction management, reliability protocols, and data warehousing. This course also continues developing the MapReduce and HDFS concepts introduced in the last course and applying them to large-scale analytics and machine learning applications within distributed systems. Learners will explore the essential components for maintaining database reliability. In addition, it will dive deeper into cloud-based data processing with Hadoop, and develop foundational skills in analytics as well as machine learning applications using collaborative filtering, clustering, and classification techniques.
2 readings
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.
11 videos5 readings3 assignments
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.
6 videos7 readings3 assignments
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.
1 video5 readings2 assignments
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