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

Learner Reviews & Feedback for Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud by University of Illinois at Urbana-Champaign

4.2
229 ratings
35 reviews

About the Course

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....

Top reviews

UN

Apr 10, 2018

My understanding of Big Data technologies was really enhanced by this course. I have decided to pursue more of these underlying technologies after this course. Good job

JA

Sep 30, 2019

Very Useful Course. Course material is massive and well prepared for the modern industry demands.

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26 - 33 of 33 Reviews for Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

By Aditya K

Sep 05, 2018

Again, too much theory. More exercises needed.

By Austin Z

Apr 26, 2019

Much better than Part 1. This course mostly shows the applications of the topics covered in the Cloud Computing Concepts course using the popular tools from when this course was recorded. There is a decent amount of redundant material from course overlap and this course could be made more concise, but there is still a decent amount of new material. You can probably pass most of the quizzes from knowledge gained in the other course though.

By Manasvi N

May 02, 2019

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By Gil S

Jun 21, 2017

course content is good, but the lectures are monotonous and put you to sleep.

By Jörg S

Mar 15, 2017

Quizzes are trivial. Makes the certification worthless.

Prof. Campbell is not a good lecturer.

The topics are treated mostly superficially, then suddenly go into too much detail sometimes (how to use IntelliJ IDEA, machine learning).

Subtitles are very buggy.

I enjoyed Mr. Farivar's talks much more, it seems like he knows what he is talking about and his presentations are well structured.

By Oleg

Sep 28, 2019

It looks like the course was influenced by reviews from 'Cloud Computing Concepts, Part 1' course that contained complaints about the programming assignment, fast pace of the course and complex quizes. (I don't share these complaints, for me that course was incredible)

This course gives a too high-level overview of the topics presented. Also, like in the previous part of the course, Mr. Campbell's lectures are too long, boring and hard to understand. This is disappointing since the topics in his lectures are really imporant. The quizes are too easy, no programming assigment, there's just no chance to check if you understood the material deep enough.

Spark, CAP theorem, Storm and a lot of other stuff is covered by Cloud Computing Concepts in a more informative and compact way.

Almost no imformation about the TensorFlow. It deserves at least a lessson devoted to it.

By Weidong X

Oct 06, 2019

I learned a little about a lot of things.

By Michał M

May 02, 2017

I've already written a review for part 1 and I have the same opinion about this one. The course is rather poor and not challenging. Only general information about relevant topics that as well read on wikipedia. No exercises, no code assignments. A lot of this content was repeated from first two parts of this specialization.