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學生對 Yandex 提供的 Big Data Essentials: HDFS, MapReduce and Spark RDD 的評價和反饋

325 個評分
88 個審閱


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....



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.


1 - Big Data Essentials: HDFS, MapReduce and Spark RDD 的 25 個評論(共 86 個)

創建者 Mayur T

Mar 08, 2019

Tools provided in the course to submit the assignment doesn't work and there is no response from the team on how to resolve this issue. All the users in this course are facing the same issue.

創建者 Ferran G P

Mar 08, 2019

The tools provide to complete the course doesn't work.

創建者 Sock, H

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.

創建者 Scott S

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.

創建者 Leonid M

Dec 12, 2018

The course is advertised as a practical one. But the majority of time is spent on outdated technologies like Map/Reduce. It would be more productive to go deeper into Spark. Assignments are not difficult but it takes a lot of time and attempts to figure out what exactly the authors wanted. The worst part is the grader and how it organized. Nevertheless you can learn a few things even if you are working in this industry.

創建者 Suman K S

Sep 26, 2018

I am unable to understand what the tutors have been talking. I am scared after seeing them talking in the very first video.

創建者 Vinokurov A

Sep 18, 2018

Difficult to understand, poorly cut and buggy

創建者 Ehsan F

Apr 02, 2019

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

創建者 Kassymzhomart K

Mar 22, 2019

Difficult for newbies, but good for intermediate

創建者 Kristin A

Feb 25, 2019

I learned some useful information and got some experience in working with Hadoop MapReduce and Apache Spark, even though there was plenty about the course that made it a real headache. I do feel like I spent much more time trying to figure out how to make my answers pass the autograder rather than learning how to structure my code to solve big data problems. Even though I won't bother to finish this course, because I don't need it to get a new job right now, I figure that what I have learned will give me a headstart if and when I really do need to learn this material.

創建者 Zhiheng L

Jan 18, 2019

fantastic content, terrible English.

創建者 navneet k

Nov 28, 2018

Awesome content...great learning ...:)

創建者 Bingnan L

Nov 15, 2018

There are a lot of unclear things about the homeworks. So even when you can run your homework successfully in the docker image, you still can't pass the online tests. Besides, the error msgs shown by online test system (not logs) are also unclear. It can't tell you the real reason of failure.

創建者 Maryna D

Oct 19, 2018

Accent is horrible, it is hard to listen, a lot of mistakes in the words pronunciation. But the idea of course is good.

創建者 Mikhail M

Oct 17, 2018

The course is not elaborated. Actually it is a FAIL. Bad accent, inappropriate jokes (Hello, Alexey!), not so good topics (for introductory course), paranoid grading system.

Only lectures from Ivan Puzyrevskiy are decent. Thank you, Ivan!

創建者 Ejaj A

Sep 03, 2018

Not very happy with the course. Submissions had lot of issues.I could not figure out and left the course in the middle(even the demo assignment was not working).The instructors were great but somehow I thought they were not very involved.Too much information (stated fast) out of which you may not be caring a lot. Lot of slides/presentation but somewhere they were too fast or were not able to connect the exact points.

創建者 Antonov A E

Aug 13, 2018

Task is easy, but takes a lot of time for debbuging on hdfs and understending whats wrong with submission.

創建者 Raja H

Jul 21, 2018

This course has been well structured and I liked the assignments a lot. One area of improvement is to make the grading environment stable. This will save a lot of time!

創建者 Evgeny F

Jul 18, 2018

Too few learning materials. Unreliable grader. Also unclear yarn mapReduce assignments. For example in the final assignment after submition one test said "... this task must be done in less than 3 jobs" Ok, why don't write it in the assignment text? And so on..

It wasn't a good learning expirience

創建者 Sergejs P

May 13, 2018

The course fills an important gap between software engineering and data engineering. The course content is good and the presentation is quite good as well. There are occasional issues with submitting assignments. It seems like the grading infrastructure has not been perfected just yet. This is definitely not a course for beginners. There's just too many things that can go wrong that are hard to understand unless you're already a somewhat experienced programmer and comfortable with the CLI.

創建者 Sanjay M

Feb 26, 2018

Excellent class to learn the basics of MapReduce, Hadoop, and PySpark. The lectures are very informative. They move at a strong pace, making this class more like a graduate level class in lecture style.

The programming problems are well designed to learn these languages.

The only downside is that code submission can be a bit of an adventure. It's not always clear exactly what the auto-grader is looking for. Aside from this issue, I would recommend this class for people interested in the material.

創建者 Ethan V

Feb 16, 2018

Interesting material, but roughly structured, and the assignments are ambitious in scope but poorly executed

創建者 Pieter M

Jan 28, 2018

The topics and the content of the course were good but that is the only thing that was good. The presentation is done in English with a heavy Russian accent with wrongly translated subtitles. The assignments are described minimalistically, passing the automatic checking of the assignments cost more time than actually getting the right answer for the assignment and often the external assignment environment is down or not functioning correctly. When this happens the staff support on the discussion board is really slow (think in days or weeks) or non-existing.

創建者 pete_ch

Jan 25, 2018

Non existent instructor support

Ambiguous instructions

Buggy inconsistent development environment.

A perfect example of how not to implement the principle of least surprise

創建者 Alexander P

Oct 29, 2017

Home Assignments and their checker could be more transparent :)