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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

322 ratings

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


Apr 9, 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


Sep 29, 2019

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

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1 - 25 of 51 Reviews for Cloud Computing Applications, Part 2: Big Data and Applications in the Cloud

By Ak D

Sep 2, 2017

This course is only informative. It provides good information of current big data technology and tool. It would be good if course also provide some assignment to complete so that course gives some hands on on technology.

By Patrick S

Jun 19, 2017

This course is really useful to get an overview of the cloud technologies if you are ether curious know what's out there, or if you are trying to determine which technologies you should focus on for the problem you are trying to solve. I believe the course is a lot more relevant if you tried out some cloud framework (i.e. play with one of the Docker or Vagrant VM demos)

The lecturers are clear, and the audio and slides are of good quality. One of the most valuable pieces of information from this course (that you cannot easily discern from reading documentation on each framework) is how the lectures link strengths or weaknesses in a technology or algorithm to its inner-workings.

One small nitpick is that the quiz questions could be improved. A lot of them is regurgitation of definitions (or regurgitation of the order of bullet points in a slide somewhere), rather than analytical style questions that require the user to think of the concepts. The end result is that the quizzes are very easy but not valuable. I assume this course had assignments before, but they appear to have been removed.

By Ning Z

Oct 28, 2020

Provides a very good overview of the essential components of distributed data processing. Popular frameworks and other tools such as Hadoop, Spark, Kafka, etc. are introduced. Several important algorithms are introduced in an animated and very accessible way, APIs and source code is also shown. Particular practical is, the course discusses which tool is best for what kind of job. The instructors are amiable and they talk in a very accessible way. Thank you very much for putting this course together!!! I enjoyed learning it.

By Yaron K

Aug 27, 2017

Introduces major Big data technologies and products and their use-cases. There are some "rough edges" as this course has clearly been built from videos from former courses, and as usual with Coursera - there are numerous errors in the subtitles/transcripts, Problematic if you're deaf or find following spoken English difficult. Still - the lecturers are very enthusiastic and you can see that they really tried hard to explain the Big data technologies - so 4 stars rounded to 5.

By Uche N

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

By Fillipe d S S

Nov 13, 2016

A very good course, with interesting topics about Big Data, Cloud Computing and MapReduce paradigm with real application examples.

By André L D d S J

Aug 5, 2020

Great course, that second part gave me a broad view of how can i build distributed systems based on each use case.

By Javed A

Sep 30, 2019

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

By Mahendra P S

Nov 27, 2017

Very good introduction of application concepts of cloud data computing. Thank You!

By Kedar G

Jul 15, 2020

Really helpful to get insights into Big Data applications

By uzair n

Oct 31, 2016

good things to learn about real world big problems

By Eduardo B L

Jun 12, 2018

The content is quite complete and challenging.

By Sudhanshu S

Dec 18, 2016

Better understanding of latest technology

By Nursultan T

Oct 29, 2016

Love this course!!!

By Ruiwen W

Aug 14, 2020

interesting course

By Sreedevi R N

Aug 6, 2020

A very good course

By shashank

Nov 13, 2018

Great for learning

By Dario F B

Nov 10, 2020

Excelent as usual

By Joseph K

Mar 30, 2019

This is amazing

By Murat K

Jul 5, 2017

Great course!

By Sarvesh G

Jan 21, 2021


By KimManSoo

Oct 5, 2018


By Raptis D

Oct 15, 2016

This course contained a great amount of information about several systems widely used nowadays for large-scale problems. There were analyses of the inner workings of these systems and their algorithms, as well as simple examples of how they can be used to solve common problems. The only drawback of the course is that the coursework was not significantly challenging and there were no programming assignments, which could give learners an opportunity to experiment with some of these technologies and acquire hands-on experience.

By Shiva B

Mar 19, 2018

Good overview and jumping off points to go explore more. Great that a lot of tool sets were exposed to us. A list of all these tool sets in a document would be handy.

By Birhanu D

Feb 23, 2020

There are a lot of technologies to cover and it is a dynamically changing subject. However, it will be great adding some hands-on exercises.