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Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames に戻る

Yandex による Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames の受講者のレビューおよびフィードバック

4.1
152件の評価
32件のレビュー

コースについて

No doubt working with huge data volumes is hard, but to move a mountain, you have to deal with a lot of small stones. But why strain yourself? Using Mapreduce and Spark you tackle the issue partially, thus leaving some space for high-level tools. Stop struggling to make your big data workflow productive and efficient, make use of the tools we are offering you. This course will teach you how to: - Warehouse your data efficiently using Hive, Spark SQL and Spark DataFframes. - Work with large graphs, such as social graphs or networks. - Optimize your Spark applications for maximum performance. Precisely, you will master your knowledge in: - Writing and executing Hive & Spark SQL queries; - Reasoning how the queries are translated into actual execution primitives (be it MapReduce jobs or Spark transformations); - Organizing your data in Hive to optimize disk space usage and execution times; - Constructing Spark DataFrames and using them to write ad-hoc analytical jobs easily; - Processing large graphs with Spark GraphFrames; - Debugging, profiling and optimizing Spark application performance. Still in doubt? Check this out. Become a data ninja by taking this course! 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....

人気のレビュー

SM

Nov 13, 2018

content of the course is remarkable and the way they explained concepts is very lucid. I just want to give suggestions please give link to the data set they are using for illustrating the concepts.

SS

Feb 03, 2018

I wish I could give more rating than 5 :). Excellent course. Thanks so much for such an excellent course. All the instructors are great.

フィルター:

Big Data Analysis: Hive, Spark SQL, DataFrames and GraphFrames: 26 - 32 / 32 レビュー

by Luis M A P

Mar 27, 2019

Good. Please fix assignments explanations. i.e In week 5.

by Adarsh G

Dec 29, 2019

Wonderful course for new learners. Thanks !!!

by Дюкарев В В

Sep 16, 2019

This is 2nd course of specialization and its better than 1st, but big big problems with outer grader system is still there :(

Another minus is that there is too much theory (graph algorithms especially) and too less practice, i think 1/10. You know theory is forgotten very fast.

Nevertheless its good, valuable course for review of technologies

by Evgenii K

May 15, 2018

Grading system it terrible, it hadn't work for a week summary, no help from staff on Coursera forum, only Slack channel could help. Theoretical material is quite good.

by ANNAMALAI A

Aug 26, 2019

Few Environment not supporting frequently.

by Li W

Aug 14, 2019

The biggest problem is the English speaking of lecturers, except week 6. It brings lots of difficulties to catch up with the notes and understand the ideas, auto subtitles even failed to translate in lots of places.

However, content is really good since week 4.

It will be great if the lecturers could illustrate or demonstrate the ideas instead of just reading the notes, as we all can read the notes.

by Григорьева М М

Nov 04, 2019

Didn't like the course for several reasons:

1. Mentors seem to leave the course. Do not expect any feedback from them.

2. Notebooks are broken for months so you can't perform assignments without docker.

3. Weeks are not consistent, the material quality differs enormally from week to week.