Johns Hopkins University
Reliability, Cloud Computing and Machine Learning
Johns Hopkins University

Reliability, Cloud Computing and Machine Learning

David Silberberg

Instructor: David Silberberg

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

20 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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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Recently updated!

December 2024

Assessments

8 assignments

Taught in English

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This course is part of the Large-Scale Database Systems Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

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.

What's included

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.

What's included

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.

What's included

1 video5 readings2 assignments

Instructor

David Silberberg
Johns Hopkins University
0 Courses0 learners

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