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.
What are BigData and distributed file systems (e.g. HDFS)?
Solving Problems with MapReduce
Solving Problems with MapReduce (practice week)
Introduction to Apache Spark
Introduction to Apache Spark (practice week)
Ivan Puzyrevskiy, Alexey A. Dral, Emeli Dral and Евгений Рябенко
The course covers a lot of useful information about Hadoop, MapReduce, and Spark, but there are some hitches, too. First, the accents of the instructors can be very thick. I found that I had to listen to each lecture twice, once to get a general sense of where the lecture was going, and then a second...
The course covers a lot of useful information about Hadoop, MapReduce, and Spark, but there are some hitches, too. First, the accents of the instructors can be very thick. I found that I had to listen to each lecture twice, once to get a general sense of where the lecture was going, and then a second time to actually understand what was being discussed. Also, the assignments might look pretty simple, but the real challenge comes in trying to get the code to meet the demands of the autograder. This was very stressful.
There is a Slack channel for the course where you are supposed to be able to seek help, but it does not appear to have anyone from the Big Data Team teaching the course checking it. It's too bad, because people do have genuine questions about how to set up big data code.
I didn't finish the course because of the autograder issues, and I think I would prefer to learn Spark in an on-person environment. My experience with this course turned me off from taking any more of the courses in the Yandex big data specialization on Coursera.
The content of the course is good, but the grading app and the whole infrastructure provided are terrible. For example, the docker image they tell you to use to work on your assignments on your local machine uses a different python version than the environment against which your code is tested. The external "Autograder" tool they use is often broken, making it stressful to reach the deadlines.
It takes forever to fix things that have nothing to do with the things you actually want to learn. Waste of time.