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Retour à Big Data Essentials: HDFS, MapReduce and Spark RDD

Avis et commentaires pour d'étudiants pour Big Data Essentials: HDFS, MapReduce and Spark RDD par Yandex

4.0
étoiles
507 évaluations
135 avis

À propos du cours

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....

Meilleurs avis

YH
21 nov. 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.

SH
9 mai 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.

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51 - 75 sur 132 Avis pour Big Data Essentials: HDFS, MapReduce and Spark RDD

par Sivakumar P

26 juin 2020

Its very useful course, good trainers are explained very detail.

par Aliaksei T

15 mai 2018

This course very nice and cool, sometimes I want just stop it =)

par amanpreet k

23 mai 2018

This week was pretty good and insightful around Map Reduce

par Álvaro L

6 mars 2019

Awesome course, goes very deep into the details.

par Irfan S

1 nov. 2017

Excellent course content covering in depth.

par Garvish

26 déc. 2017

This is really Intermediate level course.

par Navneet n

28 nov. 2018

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

par antul k

26 mai 2019

Great Content if you are a beginner.

par shubham m

20 août 2019

Very Nice..............Intraction

par kebize m

21 mars 2019

<Good learning >

par Shaik M

28 janv. 2020

good knowledge

par Aman A

30 juil. 2019

Great Course.

par Rodrigo R A d S

4 févr. 2018

Excellent!

par Alok K

16 sept. 2019

Very Good

par Anshika M

19 juin 2019

EXCELLENT

par Marwen B A

4 nov. 2019

great

par Minh T

24 août 2019

Great

par Lemohang

5 juil. 2020

test

par swapnil c

29 déc. 2018

none

par Konstantin S

16 mars 2020

The course undoubtedly provides high value and does a good job of introduction of a learner to the basics of big data technologies. I would highly recommend to do the Honors tasks along with the required assignments. There is still a room for improvement, though. The English level does not obstruct the understanding of the material but could be better. Also accurate subtitles, better graphic material, clearer task requirements and more stable grader system would be welcome. The last point might not be a problem anymore, as the grader system seem to have been overhauled in the last few weeks and have been working fine for me since. The slack support channel is active. Kudos to the teachers.

par Mohamed H

29 déc. 2019

The course is very useful and gives you the basics you need about HDFS, Map-Reduce in python (there is no java in this course) and pyspark. The assignments are straightforward, however you may face issues in the docker and in the grader system. The cons of this course is that sometimes information is given in a fast pace which somehow can make you get confused and unable to fully digest the material. Also there is no interaction at all from the instructors, it'll be nice if they can keep up with students' questions and issues in the future!

par Dorofei

14 févr. 2020

Потратил больше времени на то, чтобы Grader правильно принял решения, чем времени на решение задачи. Потратить 3-4 часа на решение исходной задачи и потратить 10 часов (включая форум и Slack) на то чтобы ответ правильно принять. Особенно на задаче с Твиттер датасетом, ругается на количество редусеров, но оказывается, надо было логи yarn тоже выводить.

Хорошо было бы добавить еще одну проверку, которая проверяет выводятся ли логи yarn и сообщать, что его нет.

Если бы не эта проблема, поставил бы 5 звезд.

par Павел С

11 déc. 2018

I think students could choose MapReduce or Spark. And about shortest path task. Provided by authors code runs out of memory while checking on cluster. After a lot of time playing with spark paramets and cache/persist i found solution without calculating all distances, but... Also there was no information about spark executors parameters on course...

Simple hint could save a lot of stupidly wasted time.

But it's not major, anyway thanks!

par Кряжевских С В

7 oct. 2019

Practice work in this course is divided in two part. First, you try to solve an assignment into your home Docker environment. It's really interesting to do it in spite of the assignments is not very clear. Second, you try to put the result into the course's grader system. For me, Grader it's like a Major Payne. You will get an amazing experience to work with production cluster through not well suited environment.

par Marco G

5 déc. 2018

Interesting, useful, informative, accessible (and sometimes funny!) lectures.

Stimulating assignments.

Fast responses from instructors/mentors.

Unfortunately, I often spent more time trying to get my assignments to pass the automatic grader than on solving them. This made the course a bit frustrating at times.