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Reliability, Cloud Computing and Machine Learning

Reliability, Cloud Computing and Machine Learning

This course is part of Large-Scale Database Systems Specialization

Instructor: David Silberberg

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 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.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

8 assignments

Taught in English

91%

of learners achieved a positive career outcome

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Build your subject-matter expertise

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

Module details

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

11 videosTotal 65 minutes

Java Example6 minutes
Formal Transaction Definition8 minutes
ACID Properties13 minutes
Distributed Transaction Management Architecture2 minutes
Two Phase Locking8 minutes
Distributed Two Phase Locking5 minutes
Timestamp Ordering Algorithm 7 minutes
Multi-version Timestamp Ordering4 minutes
Optimistic Concurrency Control4 minutes
Wait-For Graphs4 minutes
Deadlock Avoidance4 minutes

5 readingsTotal 230 minutes

Transaction Management & Concurrency Control30 minutes
Reading References60 minutes
Reading References60 minutes
Self-Reflective Reading: Concurrency Control in Database Management40 minutes
Self-Reflective Reading: Transaction Management & Concurrency Control40 minutes

3 assignmentsTotal 90 minutes

Understanding ACID Properties in Distributed Databases15 minutes
Understanding Ordering Algorithm15 minutes
Transaction Management & Concurrency Control60 minutes

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

6 videosTotal 59 minutes

Approaches to Cache Management8 minutes
Example Transaction11 minutes
Analysis and Redo Phases8 minutes
Undo Phase7 minutes
Termination Protocols15 minutes
3PC Timeout and Termination Protocols11 minutes

7 readingsTotal 315 minutes

Accumulo Architecture and Programming60 minutes
Reliability Protocols30 minutes
The Data Warehouse25 minutes
Reading References60 minutes
Reading References60 minutes
Self-Reflective Reading: ARIES Algorithm Crash During Recovery40 minutes
Self-Reflective Reading: Implementing and Optimizing Visibility Criteria Removal in Accumulo40 minutes

3 assignmentsTotal 90 minutes

ARIES Recovery and Accumulo Basics15 minutes
Reliability and Data Management in Accumulo15 minutes
Reliability Protocols, Data Warehousing, and Accumulo Architecture60 minutes

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.