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Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

Approx. 20 hours to complete

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Course Summary

This course is the second part of the Cloud Applications series and focuses on building and deploying applications on the cloud. Students will learn about containerization, microservices, and serverless architectures.

Key Learning Points

Understand containerization and how to deploy applications using Docker
Learn about microservices and how to design and deploy them on the cloud
Explore serverless architectures and how to use them for building and deploying applications

Job Positions & Salaries of people who have taken this course might have

- USA: $128,000
- India: ₹2,100,000
- Spain: €60,000
- USA: $128,000
- India: ₹2,100,000
- Spain: €60,000
- USA: $98,000
- India: ₹1,500,000
- Spain: €45,000
- USA: $128,000
- India: ₹2,100,000
- Spain: €60,000
- USA: $98,000
- India: ₹1,500,000
- Spain: €45,000
- USA: $135,000
- India: ₹2,300,000
- Spain: €65,000

Learning Outcomes

Design and deploy containerized applications using Docker
Develop and deploy microservices on the cloud
Utilize serverless architectures for building and deploying applications

Prerequisites or good to have knowledge before taking this course

Cloud Applications Part 1 course or equivalent knowledge
Familiarity with cloud computing and basic programming concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Hands-on projects and quizzes

Similar Courses

Cloud Computing Basics
Cloud Security Basics
AWS Fundamentals

Related Education Paths

Notable People in This Field

Werner Vogels
Adrian Cockcroft

Related Books

Description

Welcome to the Cloud Computing Applications course, the second part of a two-course series designed to give you a comprehensive view on the world of Cloud Computing and Big Data!

In this second course we continue Cloud Computing Applications by exploring how the Cloud opens up data analytics of huge volumes of data that are static or streamed at high velocity and represent an enormous variety of information. Cloud applications and data analytics represent a disruptive change in the ways that society is informed by, and uses information. We start the first week by introducing some major systems for data analysis including Spark and the major frameworks and distributions of analytics applications including Hortonworks, Cloudera, and MapR. By the middle of week one we introduce the HDFS distributed and robust file system that is used in many applications like Hadoop and finish week one by exploring the powerful MapReduce programming model and how distributed operating systems like YARN and Mesos support a flexible and scalable environment for Big Data analytics. In week two, our course introduces large scale data storage and the difficulties and problems of consensus in enormous stores that use quantities of processors, memories and disks. We discuss eventual consistency, ACID, and BASE and the consensus algorithms used in data centers including Paxos and Zookeeper. Our course presents Distributed Key-Value Stores and in memory databases like Redis used in data centers for performance. Next we present NOSQL Databases. We visit HBase, the scalable, low latency database that supports database operations in applications that use Hadoop. Then again we show how Spark SQL can program SQL queries on huge data. We finish up week two with a presentation on Distributed Publish/Subscribe systems using Kafka, a distributed log messaging system that is finding wide use in connecting Big Data and streaming applications together to form complex systems. Week three moves to fast data real-time streaming and introduces Storm technology that is used widely in industries such as Yahoo. We continue with Spark Streaming, Lambda and Kappa architectures, and a presentation of the Streaming Ecosystem. Week four focuses on Graph Processing, Machine Learning, and Deep Learning. We introduce the ideas of graph processing and present Pregel, Giraph, and Spark GraphX. Then we move to machine learning with examples from Mahout and Spark. Kmeans, Naive Bayes, and fpm are given as examples. Spark ML and Mllib continue the theme of programmability and application construction. The last topic we cover in week four introduces Deep Learning technologies including Theano, Tensor Flow, CNTK, MXnet, and Caffe on Spark.

Outline

Course Orientation
Welcome to Cloud Applications, Part 2!
Syllabus
About the Discussion Forums
Updating Your Profile
Social Media
Orientation Quiz

Module 1: Spark, Hortonworks, HDFS, CAP
1.1.1 Motivation for Spark
1.1.2 Apache Spark
1.1.3 Spark Example: Log Mining
1.1.4 Spark Example: Logistic Regression
1.1.5 RDD Fault Tolerance
1.1.6 Interactive Spark
1.1.7 Spark Implementation
1.2.1 Introduction to Distros
1.2.2 Hortonworks
1.2.3 Cloudera CDH
1.2.4 MapR Distro
1.3.1 HDFS Introduction
1.3.2 YARN and MESOS
Module 1 Overview
Module 1 Quiz

Module 2: Large Scale Data Storage
Module 2 Introduction
2.1.1 Introduction to MapReduce with Spark
2.1.2 MapReduce: Motivation
2.1.3 MapReduce Programming Model with Spark
2.1.4 MapReduce Example: Word Count
2.1.5 MapReduce Example: Pi Estimation & Image Smoothing
2.1.6 MapReduce Example: Page Rank
2.1.7 MapReduce Summary
2.2.1 Eventual Consistency – Part 1
2.2.2 Eventual Consistency – Part 2
2.2.3 Consistency Trade-Offs
2.2.4 ACID and BASE
2.2.5 Zookeeper and Paxos: Introduction
2.2.6 Paxos
2.2.7 Zookeeper
2.3.1 Cassandra Introduction
2.3.2 Redis
2.3.3 Redis Demonstration
2.4.1 HBase Usage API
2.4.2 HBase Internals - Part 1
2.4.3 HBase Internals - Part 2
2.4.4 Spark SQL
2.5.5 Spark SQL Demo
2.5.1 Kafka
Module 2 Overview
Module 2 Quiz

Module 3: Streaming Systems
Module 3 Introduction
3.1.1 Streaming Introduction
3.1.2 "Big Data Pipelines: The Rise of Real-Time"
3.1.3 Storm Introduction: Protocol Buffers & Thrift
3.1.4 A Storm Word Count Example
3.1.5 Writing the Storm Word Count Example
3.1.6 Storm Usage at Yahoo
3.2.1 Anchoring and Spout Replay
3.2.2 Trident: Exactly Once Processing
3.3.1 Inside Apache Storm
3.3.2 The Structure of a Storm Cluster
3.3.3 Using Thrift in Storm
3.3.4 How Storm Schedulers Work
3.3.5 Scaling Storm to 4000 Nodes
3.3.6 Q&A with Bobby Evans (Yahoo) on Storm
3.4.1 Spark Streaming
3.4.2 Lambda and Kappa Architecture
3.4.3 Streaming Ecosystem
Module 3 Overview
Module 3 Quiz

Module 4: Graph Processing and Machine Learning
4.1.1 Graph Processing
4.1.2 Pregel - Part 1
4.1.3 Pregel - Part 2
4.1.4 Pregel - Part 3
4.1.5 Giraph Introduction
4.1.6 Giraph Example
4.1.7 Spark GraphX
4.2.1 Big Data Machine Learning Introduction
4.2.2 Mahout: Introduction
4.2.3 Mahout kmeans
4.2.4 Mahout: Naïve Bayes
4.2.5 Mahout: fpm
4.2.6 Spark Naïve Bayes
4.2.7 Spark fpm
4.2.8 Spark ML/MLlib
4.2.9 Introduction to Deep Learning
4.2.10 Deep Neural Network Systems
4.3.1 Closing Remarks
Module 4 Overview
Module 4 Quiz

Summary of User Reviews

Read reviews for Cloud Applications Part 2 course on Coursera. Users have given positive feedback on this course. Many have praised its thorough coverage of cloud technologies. However, some users have mentioned that the course content is not up to date and that the assignments are challenging.

Key Aspect Users Liked About This Course

Thorough coverage of cloud technologies

Pros from User Reviews

In-depth coverage of cloud technologies
Good explanations and examples
Great for beginners
Helpful instructors
Good pace

Cons from User Reviews

Course content not up to date
Challenging assignments
Not enough hands-on coding
Some lectures are repetitive
Some concepts are difficult to understand

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