About this Course
319개의 평가
86개의 리뷰

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 44시간 필요

권장: 6 weeks of study, 6-8 hours/week...


자막: 영어

귀하가 습득할 기술

Python ProgrammingApache HadoopMapreduceApache Spark

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 44시간 필요

권장: 6 weeks of study, 6-8 hours/week...


자막: 영어

강의 계획 - 이 강좌에서 배울 내용

완료하는 데 24분 필요


8 videos (Total 14 min), 1 reading
8개의 동영상
Issues BigData can solve1m
BigData Applications1m
What is BigData Essentials?2m
Course Structure2m
Meet Emeli1m
Meet Alexey2m
Meet Ivan1m
1개의 읽기 자료
Slack Channel is the quickest way to get answers to your questions10m
완료하는 데 8시간 필요

What are BigData and distributed file systems (e.g. HDFS)?

18 videos (Total 136 min), 9 readings, 5 quizzes
18개의 동영상
File system managing6m
File content exploration 15m
File content exploration 213m
Scaling Distributed File System9m
Block and Replica States, Recovery Process 16m
Block and Replica States, Recovery Process 27m
HDFS Client9m
Namenode Architecture8m
Text formats9m
Binary formats 18m
Binary formats 28m
How to submit your first assignment3m
How to Install Docker on Windows 7, 8, 104m
9개의 읽기 자료
Basic Bash Commands10m
HDFS Lesson Introduction10m
Gentle Introduction into "curl"10m
File formats extra (optional)10m
Grading System: Instructions and Common Problems10m
Docker Installation Guide10m
Programming Assignment: Instructions and Common Problems10m
FAQ How to show your code to teaching staff10m
Slack channel "Bigdata-coursera" - the quickest to solve technical problems.10m
2개 연습문제
Distributed File Systems16m
Big Data and Distributed File Systems25m
완료하는 데 3시간 필요

Solving Problems with MapReduce

17 videos (Total 94 min), 1 reading, 3 quizzes
17개의 동영상
Unreliable Components 28m
Distributed Shell8m
Fault Tolerance7m
Fault Tolerance. Live Demo3m
Streaming in Python3m
WordCount in Python5m
Distributed Cache4m
Environment, Counters4m
Speculative Execution / Backup Tasks3m
1개의 읽기 자료
Hadoop Streaming Assignments: Intro and Code Samples10m
3개 연습문제
Hadoop MapReduce Intro26m
MapReduce Streaming26m
Hadoop Streaming Final30m
완료하는 데 4시간 필요

Solving Problems with MapReduce (practice week)

1 video (Total 3 min), 5 readings, 5 quizzes
5개의 읽기 자료
Hadoop Streaming Assignments: Intro and Code Samples10m
Hints to Debug Hadoop Streaming Applications10m
Grading System and Grading System Sandbox User Guide10m
Hadoop Streaming Assignments: Instructions10m
Hint to the "Stop words" programming assignment10m
완료하는 데 3시간 필요

Introduction to Apache Spark

16 videos (Total 95 min), 2 readings, 2 quizzes
16개의 동영상
Transformations 16m
Transformations 27m
Execution & Scheduling6m
Caching & Persistence5m
Broadcast variables5m
Accumulator variables5m
Getting started with Spark & Python6m
Working with text files6m
Broadcast & Accumulator variables5m
Spark UI4m
Cluster mode3m
2개의 읽기 자료
Spark Assignments Intro10m
Instructions for Spark programming assignment10m
2개 연습문제
Lesson 1 Quiz20m
Lesson 2 Quiz24m
완료하는 데 7시간 필요

Introduction to Apache Spark (practice week)

2 readings, 2 quizzes
2개의 읽기 자료
Spark assignments Intro10m
Building an intuition behind the PMI definition10m
완료하는 데 11시간 필요

Real-World Applications

9 videos (Total 59 min), 2 readings, 5 quizzes
9개의 동영상
Estimating proportions7m
Map and Reduce Side Joins9m
Tabular Data, KeyFieldSelection8m
Data Skew, Salting4m
Twitter graph case study6m
Shortest path7m
2개의 읽기 자료
Data and code10m
Starter for "Reconstructing the path" assignment10m
3개 연습문제
Sample estimates10m
Advanced MapReduce Techniques20m
Real-World Applications10m
86개의 리뷰Chevron Right


이 강좌를 수료한 후 새로운 경력 시작하기


이 강좌를 통해 확실한 경력상 이점 얻기


급여 인상 또는 승진하기

최상위 리뷰

대학: YHNov 22nd 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.

대학: SHMay 10th 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.



Ivan Puzyrevskiy

Technical Team Lead

Alexey A. Dral

Founder and Chief Executive Officer
BigData Team

Yandex 정보

Yandex is a technology company that builds intelligent products and services powered by machine learning. Our goal is to help consumers and businesses better navigate the online and offline world....

Big Data for Data Engineers 전문 분야 정보

This specialization is made for people working with data (either small or big). If you are a Data Analyst, Data Scientist, Data Engineer or Data Architect (or you want to become one) — don’t miss the opportunity to expand your knowledge and skills in the field of data engineering and data analysis on the large scale. In four concise courses you will learn the basics of Hadoop, MapReduce, Spark, methods of offline data processing for warehousing, real-time data processing and large-scale machine learning. And Capstone project for you to build and deploy your own Big Data Service (make your portfolio even more competitive). Over the course of the specialization, you will complete progressively harder programming assignments (mostly in Python). Make sure, you have some experience in it. This course will master your skills in designing solutions for common Big Data tasks: - creating batch and real-time data processing pipelines, - doing machine learning at scale, - deploying machine learning models into a production environment — and much more! Join some of best hands-on big data professionals, who know, their job inside-out, to learn the basics, as well as some tricks of the trade, from them. Special thanks to Prof. Mikhail Roytberg (APT dept., MIPT), Oleg Sukhoroslov (PhD, Senior Researcher, IITP RAS), Oleg Ivchenko (APT dept., MIPT), Pavel Akhtyamov (APT dept., MIPT), Vladimir Kuznetsov, Asya Roitberg, Eugene Baulin, Marina Sudarikova....
Big Data for Data Engineers

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.