Chevron Left
Back to Big Data Essentials: HDFS, MapReduce and Spark RDD

Big Data Essentials: HDFS, MapReduce and Spark RDD, Yandex

324 ratings
87 reviews

About this Course

Have you ever heard about such technologies as HDFS, MapReduce, Spark? Always wanted to learn these new tools but missed concise starting material? Don’t miss this course either! In this 6-week course you will: - learn some basic technologies of the modern Big Data landscape, namely: HDFS, MapReduce and Spark; - be guided both through systems internals and their applications; - learn about distributed file systems, why they exist and what function they serve; - grasp the MapReduce framework, a workhorse for many modern Big Data applications; - apply the framework to process texts and solve sample business cases; - learn about Spark, the next-generation computational framework; - build a strong understanding of Spark basic concepts; - develop skills to apply these tools to creating solutions in finance, social networks, telecommunications and many other fields. Your learning experience will be as close to real life as possible with the chance to evaluate your practical assignments on a real cluster. No mocking, a friendly considerate atmosphere to make the process of your learning smooth and enjoyable. Get ready to work with real datasets alongside with real masters! Special thanks to: - Prof. Mikhail Roytberg, APT dept., MIPT, who was the initial reviewer of the project, the supervisor and mentor of half of the BigData team. He was the one, who helped to get this show on the road. - Oleg Sukhoroslov (PhD, Senior Researcher at IITP RAS), who has been teaching MapReduce, Hadoop and friends since 2008. Now he is leading the infrastructure team. - Oleg Ivchenko (PhD student APT dept., MIPT), Pavel Akhtyamov (MSc. student at APT dept., MIPT) and Vladimir Kuznetsov (Assistant at P.G. Demidov Yaroslavl State University), superbrains who have developed and now maintain the infrastructure used for practical assignments in this course. - Asya Roitberg, Eugene Baulin, Marina Sudarikova. These people never sleep to babysit this course day and night, to make your learning experience productive, smooth and exciting....

Top reviews


Nov 22, 2018

Everything in this course is new to me, but it provides me with many practice so I can gradually get familiar with all these new stuff. I find it a bit challenging, but overall it's quite good.


May 10, 2019

The course takes you from basic level , step level .But It is quite fast for beginners , you may need pause video in between and try to understand the concept.

Filter by:

84 Reviews

By Rain

May 24, 2019


By Waldemar Druszcz

May 19, 2019

good course, covering a lot of foundations for Big Data and for Hadoop/Spark. Also one of the few that focus on Data Engineering perspective rather than Data Science. Learned a lot here!

By Artem Seleznev

May 17, 2019

The nice and helpful course as usual because it was made by Yandex and BDTeam

By Nagarajan

May 14, 2019

The course content is good, but you will have a horrible time with the grader system. You will have to spend lot of additional hours which you shouldn't be. I could have learned a lot more if the assignments are clear and if the mentors are active. Many links are broken as well.

By Hitesh Gaud

May 13, 2019

Assignment tool were not working. In every week there is problem.

By Somashekhar Hatti

May 10, 2019

The course takes you from basic level , step level .But It is quite fast for beginners , you may need pause video in between and try to understand the concept.

By Scott Small

Apr 05, 2019

I audited this course, because I was interested to complete the specialization. I finished the course and all of the assignments. After finishing this course, I will not continue the specialization.

For me, the biggest problem was the lectures regarding MapReduce. In my mind, there was a disconnect between the lecture materials and the assignments. The assignments also tended to be poorly worded; it was rarely clear what needed to be done to finish an assignment. I needed to use a lot of external resources here. I still do not understand map-side and reduce-side joins, and I do not feel comfortable writing a MapReduce job without a lot of time.

The lectures over Hadoop were ok, but strange. A lot of details are presented about how Hadoop works internally, and the speed at which the lectures move makes the discussion very dense and difficult to follow. However, the material is not used in the assignments or required further in the course, and the instructors are quite clear that this is the case. To me, this seems like a missed opportunity. There could be an entire week dedicated to the internals of Hadoop (or maybe even an entire course). After this course, I do feel comfortable getting around in an HDFS, and I feel I have a basic understanding of how it works.

The best part of the course was the lectures about Spark. The material was clearly presented, and the assignments were all relevant. The course gives a good introduction of Spark. I feel comfortable using basic SPARK operations to manipulate data.

If you wish to take this course, I recommend that you are knowledgeable about Linux Bash commands. There is a review section, but if you are seeing these ideas for the first time, I suspect you will suffer a lot.

The instructors provide Docker images so the assignments can be completed on a local computer. If you are not knowledgeable about Docker, I recommend learning through this course. It's not necessary but it's quite simple.

I do agree with others that the accents of some instructors can be difficult to understand. There are options for English subtitles which help a lot here.

Because I only audited the course, I could not submit any assignments for review. Thus I cannot comment on the automated grader. However many people in the forums complained about the grader.

I am interested to continue with Big Data topics, but this course was an inefficient way to learn. I fear the remaining courses in the specialization will be similar. I have completed several courses on Coursera, and this was by far the worst. I recommend the MapReduce section be improved and clarified.

By Ehsan Fathi

Apr 02, 2019

This is the most awful course I have ever had in Coursera

By Sock, Hanbin

Mar 25, 2019

I appreciate that practical assignments exist and were definitely helpful for really understanding how to use MapReduce and Spark.

My complaints come from various issues that shouldn't be issues. A link to a Jupyter notebook file for the statistics part on week 6 wouldn't be downloaded when clicked on, and instead opened it on a new page (and the notebook file did not work unless you copied and pasted the page AFTER VIEWING THE PAGE's SOURCE).

The URLs for the New York taxi data are completely broken, the auto marker gives unhelpful error messages (for example, for week 6 td*idf when the issue was that I redirected my first map reduce job's stderr to a file, the error message from the marker was to "use only 0 or >1 reducers". I was using 0 reducers already, so this error message confused me for hours until I found a random post on Slack that said that stderr is needed to be output to terminal for the first mapreduce).

The course does teach quite a bit, however the lack of support from instructors, poor error messages on auto testers, and other issues that you will naturally encounter taking the course make it difficult for me to recommend this course to others.

By Kassymzhomart Kunanbayev

Mar 22, 2019

Difficult for newbies, but good for intermediate